Systems, devices, and methods for generating and manipulating objects in a virtual reality or multi-sensory environment to maintain a positive state of a user

ABSTRACT

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/222,873, filed Jul. 16, 2021, U.S. Provisional Application No.63/225,152, filed on Jul. 23, 2021, and U.S. Provisional Application No.63/245,625, filed Sep. 17, 2021, the entire disclosures of each of whichis incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates to systems, devices, and methods forgenerating and manipulating objects in a virtual reality ormulti-sensory environment, and in particular, relates to the field ofdata processing and artificial intelligence, e.g., for treating a mentaland/or physical condition. More particularly, the embodiments describedherein relate to data processing and machine learning methods andapparatuses for generating multi-sensory environments and providingadaptive digital therapy, e.g., in conjunction with drug treatment, andfor providing patient monitoring and feedback associated with theadaptive digital therapy to ensure suitable mindsets or states of auser.

BACKGROUND

Drug treatments have been used to treat many different types of medicalconditions and disorders. Some known drug treatments can oftentimes takeweeks or months to achieve their full effects, and in some instances mayrequire continued use or lead to drug dependencies or othercomplications. Psychotherapy and other types of human interactions canbe useful for treating disorders, e.g., for improving an effectivenessof a drug treatment, mitigating/reducing the complications associatedwith a drug treatment or medical procedure, or generally improving anindividual's wellbeing. But such interactions may be limited by theavailability of trained professionals and vary in effectivenessdepending on skills, time availability of the trained professional andpatient, and/or specific techniques used by trained professionals. Thus,a need exists for improved systems and methods for treating medicalconditions and disorders, as well as improving the safety profile and/orresponse to various medical procedures. Additionally, as psychedelicsand other types of compounds are being developed as treatments forneuropsychiatric disorders, designing the right set and setting areimportant considerations. Set and setting are factors relevant to theexperience, safety, and outcomes that occur when people use psychedelicdrugs. Set refers to temperament, groundwork, expectation of the personhaving the experience and setting refers to the physical, social andcultural environment in which the experience takes place.

SUMMARY

Systems, devices, and methods described herein relate to multi-sensorypresentation devices, including virtual reality (VR) devices, visualdisplay devices, sound devices, haptic devices, and other forms ofpresentation devices, that are configured to present sensory elements,including visual and/or audio scenes, to a user.

In some embodiments, an apparatus can include a virtual reality (VR)device configured to present at least one of a visual, olfactory,gustatory, auditory, or haptic signal to a user; a set of sensorsconfigured to measure user data, the set of sensors including at leastan electroencephalography (EEG) sensor and/or a photoplethysmography(PPG) sensor; a memory; and a processor operatively coupled to thevirtual reality device, the set of sensors, and the memory. Theprocessor is configured to: present, using the VR device, a sceneincluding a first set of objects to the user, the first set of objectsincluding visual, olfactory, gustatory, auditory, or haptic elements;instruct a user to engage in an activity for increasing focus and/orrelaxation; after the user has been instructed to engage in theactivity, iteratively perform until a score indicative of a state of theuser satisfies a metric: measuring, using the set of sensors, the userdata including at least EEG data and/or heart rate variability (HRV)data; determining, using a model trained to measure the state of theuser, and based on the user data, the score of the user; and in responseto the score of the user being above a threshold, modifying, using theVR device, the presentation of the first set of objects such that thefirst set of objects form or follow a pattern or presenting, using theVR device, an additional object; and in response to the score of theuser satisfying the metric, present, using the VR device, a second setof objects to the user.

In some embodiments, an apparatus can include a virtual reality (VR)device configured to present at least one of a visual, olfactory,gustatory, auditory, or haptic signal to a user; one or moreelectroencephalography (EEG) sensors configured to measure EEG data ofthe user; a memory; and a processor operatively coupled to the virtualreality device, the one or more EEG sensors, and the memory. Theprocessor is configured to: present, using the VR device, a sceneincluding a first set of objects to the user, the first set of objectsincluding visual, olfactory, gustatory, auditory, or haptic elements;instruct a user to engage in an activity for increasing focus and/orrelaxation; after the user has been instructed to engage in theactivity, iteratively perform until a score indicative of a state of theuser satisfies a metric: measuring, using the one or more EEG sensors,the EEG data; determining, using a model trained to measure the state ofthe user, and based on the EEG data, the score of the user; and inresponse to the score of the user being above a threshold, modifying,using the VR device, the presentation of the first set of objects suchthat the first set of objects form or follow a pattern or presenting,using the VR device, an additional object; and in response to the scoreof the user satisfying the metric, present, using the VR device, asecond set of objects to the user.

In some embodiments, a method can include presenting, using a virtualreality (VR) device, a scene including a first set of objects to theuser, the first set of objects including visual, olfactory, gustatory,auditory, or haptic elements; instructing a user to engage in anactivity for increasing focus and/or relaxation; after the user has beeninstructed to engage in the activity, iteratively performing until ascore indicative of a state of the user satisfies a metric: measuring,using a set of sensors including an electroencephalography (EEG) sensorand/or a photoplethysmography (PPG) sensor, the user data including atleast EEG data and/or heart rate variability (HRV) data; determining,using a model trained to measure the state of the user, and based on theuser data, the score of the user; and in response to the score of theuser being above a threshold, modifying, using the VR device, thepresentation of the first set of objects such that the first set ofobjects form or follow a pattern or presenting, using the VR device, anadditional object; and in response to the score of the user satisfyingthe metric, presenting, using the VR device, a second set of objects tothe user.

In some embodiments, an apparatus can include: a multi-sensorypresentation device configured to present at least one of a visual,olfactory, gustatory, auditory, or haptic signal to a user; a set ofsensors configured to measure user data, the set of sensors including atleast an electroencephalography (EEG) sensor and/or aphotoplethysmography (PPG) sensor; a memory; and a processor operativelycoupled to the memory, the multi-sensory presentation device, and theset of sensors. The processor configured is to: present, using themulti-sensory presentation device and after the user has received a drugtreatment, a scene to the user, the scene including a first set ofvisual, olfactory, gustatory, auditory, or haptic elements; while thescene is being presented, iteratively perform: measuring, using the setof sensors, user data including at least EEG data and/or heart ratevariability (HRV) data; determining, using a model trained to measure astate of the user, and based on the user data, a score of the userindicative of a state of the user; in response to the score being lowerthan a threshold, modifying, based on the score of the user, the sceneto include a second set of visual, olfactory, gustatory, auditory, orhaptic elements different from the first set of visual, olfactory,gustatory, auditory, or haptic elements; and in response to the scorebeing greater than the threshold for a set period of time, modifying thescene to include a third set of visual, olfactory, gustatory, auditory,or haptic elements different from the first and second sets of visual,olfactory, gustatory, auditory, or haptic elements; and continue topresent the scene to the user until a predetermined period of time haselapsed from the user receiving the drug treatment.

In some embodiments, an apparatus can include a multi-sensorypresentation device configured to present at least one of a visual,olfactory, gustatory, auditory, or haptic signal to a user; one or moreelectroencephalography (EEG) sensors configured to measure EEG data ofthe user; a memory; and a processor operatively coupled to the memory,the multi-sensory presentation device, and the one or more EEG sensors.The processor is configured to: present, using the multi-sensorypresentation device and after the user has received a drug treatment, ascene to the user, the scene including a first set of visual, olfactory,gustatory, auditory, or haptic elements; while the scene is beingpresented, iteratively perform: measuring, using the one or more EEGsensors, the EEG data; determining, using a model trained to measure astate of the user, and based on the EEG data, a score of the userindicative of a state of the user; in response to the score being lowerthan a threshold, modifying, based on the score of the user, the sceneto include a second set of visual, olfactory, gustatory, auditory, orhaptic elements different from the first set of visual, olfactory,gustatory, auditory, or haptic elements; and in response to the scorebeing greater than the threshold for a set period of time, modifying thescene to include a third set of visual, olfactory, gustatory, auditory,or haptic elements different from the first and second sets of visual,olfactory, gustatory, auditory, or haptic elements; and continue topresent the scene to the user until a predetermined period of time haselapsed from the user receiving the drug treatment.

In some embodiments, a method can include: presenting, using amulti-sensory presentation device and after the user has received a drugtreatment, a scene to the user, the scene including a first set ofvisual, olfactory, gustatory, auditory, or haptic elements; while thescene is being presented, iteratively performing: measuring, using a setof sensors including an electroencephalography (EEG) sensor and/or aphotoplethysmography (PPG) sensor, user data including at least EEG dataand/or heart rate variability (HRV) data; determining, using a modeltrained to measure a state of the user, and based on the user data, ascore of the user indicative of a state of the user; in response to thescore being lower than a threshold, modifying, based on the score of theuser, the scene to include a second set of visual, olfactory, gustatory,auditory, or haptic elements different from the first set of visual,olfactory, gustatory, auditory, or haptic elements; and in response tothe score being greater than the threshold for a set period of time,modifying the scene to include a third set of visual, olfactory,gustatory, auditory, or haptic elements different from the first andsecond sets of visual, olfactory, gustatory, auditory, or hapticelements; and continuing to present the scene to the user until apredetermined period of time has elapsed from the user receiving thedrug treatment.

In some embodiments, a method can include receiving, at a computedevice, user data from sensors associated with a user during apresentation of content or interaction with content such as a digitaltherapy. The method can further include executing a machine learningmodel to determine an indication of state associated with the digitaltherapy based on the user data, the indication of state indicating ameasure of progression within the digital therapy and/or toward anoptimal mental state. The method can further include determining amodification to digital therapy based on the indication of state. Themethod can also include using that said state to monitor and adjust thesetting. It can also include more specifically in the context of usingpsychedelics maintaining the right set and setting using adaptivedigital therapy to achieve positive long-term well-being.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is a schematic block diagram of a therapy system, according to anembodiment.

FIG. 2 is a flow chart illustrating the flow of data between componentsof a therapy system, according to an embodiment.

FIG. 3 is a flow chart illustrating the flow of data between componentsof a therapy system to produce a personalized classifier, according toan embodiment.

FIG. 4 is a flow chart illustrating a method of adaptive digital therapyfor use with a drug treatment, according to an embodiment.

FIG. 5 is a schematic diagram illustrating a closed-loop for a digitaltherapy, according to an embodiment.

FIG. 6 is a graph of raw electroencephalography (EEG) data includingartifacts, and

FIG. 7 is a graph of EEG data after preprocessing to remove theartifacts, according to an embodiment.

FIG. 8 is a graph of EEG data that fails an impedance check, accordingto embodiments.

FIGS. 9A and 9B depict a method of implementing a feedback-basedmeditation program, according to embodiments.

FIG. 10 depicts a method of implementing a feedback-based adaptivesettings system, according to embodiments.

FIGS. 11A-11E depict different stages or points in an example scene usedin a feedback-based meditation program, according to embodiments.

FIGS. 12A-12B depict different stages or points in another example sceneused in a feedback-based meditation program, according to embodiments.

FIGS. 13A-13E depict different stages or points in another example sceneused in a feedback-based meditation program, according to embodiments.

FIGS. 14A-14D depict different stages or points in another example sceneused in a feedback-based meditation program, according to embodiments.

DETAILED DESCRIPTION

Described herein are one or more systems and methods relating tofeedback-based digital therapies or interventions, e.g., to be used toprepare for and/or with drug treatments or medical procedures (e.g.,imaging sessions, surgical procedures, etc.), and/or for improvingwellbeing. For example, one or more system and methods described hereincan be used prior to or with a psychedelic therapy or treatment, e.g.,to treat a mental health disorder, depression, anxiety, substance abusedisorder, etc. In some embodiments, one or more systems and methodsdescribed herein relate to providing adaptive digital therapy inconjunction with drug treatment and providing a user (e.g., patient)monitoring and feedback associated with such therapies and/or treatmentto adapt and ensure a suitable mindset and setting for such treatment.

A set of a user prior to and after receiving a drug treatment can be auseful variable. The set can relate to a user's mindset. A user with apositive set can feel well prepared, in a good mood, and ready and opentoward an upcoming experience and have low levels of anxiety before drugintake. Users with positive sets are more likely to have lesschallenging experiences, and oftentimes may have peak experiences or apositive acute experience. On the other hand, a user with a negativeset, such as a user with emotional excitability, anxiety, and/orapprehension, may be more likely to have a challenging experience.Therefore, training the user to have a suitable set prior to and/orafter receiving a drug therapy or treatment can increase safety and/orefficacy of receiving the treatment. Such can also be the case withother types of procedures, e.g., medical procedures such as imagingsessions (e.g., with computed tomography (CT), magnetic resonanceimaging (MRI), etc.), surgical procedures, medical exams, etc. One ormore systems and methods described herein can be used to induce apredefined set (e.g., state) and setting for receiving a drug treatment(e.g., a psychedelic drug), including, but not limited to, for example,Psilocybin, Ketamine, Esketamine, R-Ketamine, RL-007 (e.g., forschizophrenia), Ibogaine, Deuterated Etifoxine, N-Acetylcysteine,methylenedioxy-methyl amphetamine (MDMA),N-methyl-1-(3,4-methylenedioxyphenyl)propan-2-amine),methylenedioxy-methylamfetamine, 3,4-methylenedioxymethamphetamine,3,4-Methylenedioxyamphetamine (MDA), Salvinorin A, DeuteratedMitragynine, Noribogaine, Dimethyltryptamine (DMT), N,N-DMT,D-Cycloserine, or other drug treatments with acute effects on a centralnervous system.

The one or more therapy systems and methods described herein can providereal-time feedback and adaptive training on the user's progress towardsa specific cognitive, emotional and physiological change, helping toincrease their interoceptive awareness. As the user observes progresswithin a session and/or over multiple sessions, the user's perceivedlocus of control in self-regulating physiological activity and emotioncan shift inwards and the user's self-efficacy can increase. A repeateduse of one or more therapy systems and methods described herein canchange the user's mindset, enable a better ability to self-regulateemotion, improve the user's ability to physiologically react to andrecover from stressful situations, and become internalized, that overallcan result in lower levels of trait anxiety and improve optimal mentalstates.

In some embodiments described herein, set and setting are involved indetermining acute effects after receiving a drug treatment, such as, forexample, the effects of a psychedelic, and/or long-term emotional andmental benefits of a drug treatment. For example, in addition to anindividual's personal traits, the actual experience upon immediate onsetof a psychedelic can be predictive of improved well-being at, forexample, 2 and 4-week follow-ups. The acute experience is, in part,determined by an individual's set and setting. In some instances, acuteexperiences can be positively correlated to longer-term well-being.Specifically, higher ratings of peak experiences, described asexperiencing disorientation in space and time, feelings of being free ofinner conflict, feelings of awe, amazement and humility, and a sense ofoneness with the universe, can have a long-term positive effect on thechange in well-being after a psychedelic experience. Experiencesdescribed as challenging, characterized by anxiety, psychologicalstruggle, fear, panic and/or paranoia, can negatively influencewell-being and impact a safety profile of a drug or drug treatmentsession.

As noted above, with a positive set, a user can feel well prepared,relaxed, in a good mood, ready and open towards the upcoming experienceand have had low levels of anxiety right before drug intake, and isassociated with less challenging experiences and predictive of peakexperiences. A user that can have peak experiences is often lesshostile, tense, and anxious prior to dosing. Positive prior expectationscan increase the likelihood of a positive acute experience. Theindividuals with positive prior expectations are more willing toconfront anxiety and less frightened by the prospect ofself-confrontation. On the other hand, a negative set can becharacterized by emotional excitability, anxiety and apprehension.Therefore, a negative set can be predictive of challenging experiences.A user with higher levels of apprehension and anticipatory anxiety priorto dosing is more likely to have acute anxiety and/or experiencepsychological discomfort during a psychedelic experience. In addition,comfort in a setting and comfort with the people present during thepsychedelic experience can be predictive of higher well-being scores.Sensor data collected and processed in substantially real-time andduring a presentation of content or digital therapy, as describedherein, can be used to determine and adapt a set and/or a setting beforeand/or during a psychedelic treatment, and therefore, lead to improvedand safer acute experience and/or improved long-term well-being outcome.Optimal sets or mental states, before and/or during a psychedelicexperience and/or prior to dosing, can include, but is not limited to,relaxation, low emotional excitability, and/or focus.

The one or more systems and methods described herein can utilize aclosed-loop control system that can have multiple input and multipleoutput (MIMO) to induce and/or maintain a target mental state/optimalset for psychedelic treatment using brain-computer interface (BCI). Inaddition, in some instances, the one or more systems and methodsdescribed herein can analyze user data obtained using a multimodalsensor(s) that measure one or more aspects of human physiology andbiochemistry input including, but not limited to, heart rate, heart ratevariability, galvanic skin response, respiratory rate, eye movements,facial expressions, glucose, and/or pupillometry, to evaluate one ormore outputs including, but not limited to, visual, olfactory,gustatory, auditory, haptic systems, and/or the like, to promote theright set and setting for responding to psychedelics and other drugstreatments with acute effects on the central nervous system.

Therefore, in some embodiments, the one or more systems and methodsdescribed herein can induce and/or maintain an optimal or improved setand setting, e.g., for experiencing psychedelic treatment, inpreparation for a medical procedure, etc. The method can includeperforming an adaptive setting including auditory, visual, olfactory,gustatory and/or haptic experience feature systems. The method canfurther include obtaining, using a system, sensor data of a user (e.g.,a patient) during the acute psychedelic experience. The method canfurther include determining, using a system, whether the sensor dataincludes information indicative of desired and/or optimal set functions(i.e., relaxed, focused, unagitated, positive affect, and/or the like)used for controlling the adaptive setting via changes to or maintainingthe current state of the auditory, visual, olfactory, gustatory, hapticsystems, and/or the like, to induce and/or maintain the desired and/oroptimal set when it is determined that the sensor data includes theinformation indicative of desired and/or optimal set functions. Themethod can further include calculating an assessment/score of the userwith regard to an improved or optimal set that controls the adaptivesetting in the form of changes to or maintaining the current state ofthe auditory, visual, olfactory, gustatory and/or haptic systems. Themethod can further include adapting the adaptive setting output to eachindividual based on past calculated scores and past responses to variousstates of the adaptive setting. The method can further includepersonalizing the assessment/score to the needs and unique conditions ofeach user.

Moreover, the one or more systems and methods described herein canimprove the safety profile and potentially improve/extend an efficacyand/or sustainability of response from various psychedelic treatments.The one or more systems and methods described herein can improvecognitive and emotional safety and well-being throughout a psychedelicexperience by actively monitoring a patient's mental state andaddressing factors that may cause a negative acute psychedelicexperience. Specifically, the one or more systems and methods describedherein can assist a user in entering and maintaining a state such as,for example, relaxed, focused, unagitated, positive affect, or reducedemotional excitability, and can provide the user with positive andcomfortable environments that help to induce and maintain this mentalstate/set.

FIG. 1 is a block diagram that illustrates a therapy system 100,according to an embodiment. The therapy system 100 includes a therapydevice 110, sensor(s) 120, and a compute device 130 that collectivelycan adaptively present multi-sensory elements, e.g., that are part of adigital therapy or content (e.g., a digital exercise, digital settings,etc.), to a user. The multi-sensory elements can include, for example,one or more visual, olfactory, gustatory, auditory, or haptic signals orelements. In some implementations, the therapy device 110, the sensor(s)120, and the compute device 130 can communicate with one another via anetwork (not shown). The network can be any type of network (e.g., alocal area network (LAN), a wide area network (WAN), a virtual network,a telecommunications network) implemented as a wired network and/orwireless network and used to operatively couple the devices. In someimplementations, the therapy device 110, the sensor(s) 120, and thecompute device 130 can communicate using one or more direct (e.g., notusing an intermediary device such as, for example, a router)electromagnetic communications (e.g., one or more Bluetooth™communication channels between the therapy device 110, the sensor(s)120, and the compute device 130).

The therapy device 110 and the compute device 130, each can be orinclude, but is not limited to, for example, a cellular telephone (e.g.,smartphone), a tablet computer, a laptop computer, a desktop computer, aportable media player, an audio device, a wearable digital device (e.g.,digital glasses, wristband, wristwatch, brooch, armbands, virtualreality/augmented reality headset, a tactile vest), a projector or otherdisplay device, an audio device such as headphones, and/or a scentsimulation device, etc. In some implementations, one or more of thedevices in the therapy system 100 (e.g., the therapy device 110, thesensor 120, and the compute device 130) can include a user interface(e.g., a graphical user interface (GUI), a mouse, a keyboard, atouchpad, a virtual reality headset, an augmented headset, hapticinterface, and/or a microphone) that enables a user to control theoperation of the devices connected thereto as described in more detailherein. While not shown in FIG. 1 , in some embodiments the therapydevice 110 can be part of the compute device 130. In some embodiments,the therapy device 110 can be implemented as a virtual reality (VR)device or a multi-sensory device. The therapy device 110 can beconfigured to generate and present one or more visual, olfactory,gustatory, auditory, or haptic signals or elements. In some embodiments,the therapy device 110 can include one or more of: a projector ordisplay, a scent delivery device or system, a tactile device (e.g., atactile vest, armband, headband, headset, etc.), or an audio device(e.g., headphones, speakers, etc.).

The sensor(s) 120 can include any suitable component that capturesinformation about a user, an environment of the user, objects in theenvironment around the user and/or the compute device 130. The sensor(s)120 can be or include multi-modal sensor(s). In some embodiments, thesensor(s) 120 can include a brain-computer interface (BCI), anelectroencephalography (EEG) device, a photoplethysmography (PPG)sensor, or other physiological sensors, behavioral sensors, orenvironmental sensors. The sensor(s) 120 can include, but is not limitedto, for example, an electrode that is attached to the head of a user(e.g., a patient). The sensor(s) 120 can measure, but is not limited to,for example, EEG, electrooculography, electromyography (EMG), pulseoximetry, electrocardiogram (EKG), respiratory rate, eye movements,pupillometry, glucose, heart rate, heart rate variability (HRV), PPG,blood pressure, blood pressure variability, baroreflex sensitivity,electrodermal activity (EDA), galvanic skin response (GSR), and/or bodytemperature.

In some implementations, the sensor(s) 120 can include, but is notlimited to, for example, image capture devices (e.g., cameras), ambientlight sensors, audio devices (e.g., microphones), light sensors (e.g.,photodetectors), proprioceptive sensors, position sensors, tactilesensors, force or torque sensors, temperature sensors, pressure sensors,motion sensors, sound detectors, gyroscopes, accelerometers, bloodoxygen sensors, metabolic sensors, glucose monitoring sensors, orcombinations thereof. The sensor(s) 120 can measure, for example, one ormore of motion data, mobile device data (e.g., digital exhaust,metadata, device use data), wearable device data, geolocation data,sound data, camera data, therapy/training session data, medical recorddata, input data, environmental data, application usage data, attentiondata, arousal data, valence data, activity data, sleep data, nutritiondata, menstrual cycle data, cardiac data, heart rate data, heart ratevariability data, social functioning data, and/or facial expressiondata. While not shown in FIG. 1 , in some embodiments the sensor(s) 120can be part of (e.g., integrated into) the compute device 130 and/or thetherapy device 110.

The compute device 130 can include a memory 131, a processor 132, and acommunication interface. The memory 131 can store data (e.g., user data137, setting data 138, the drug data 139) and/or a set of codes. Theprocessor 132 can be operatively coupled to the memory 131, and canprocess the data and execute the set of codes. The compute device 130can further include one or more input portions (not shown) that receiveat least a portion of the data 137 from the sensor(s) 120. The one ormore input portions can be/include, but are not limited to, for example,an antenna that receives electromagnetic waves (e.g., Bluetooth™ signalsand/or WiFi™ signals), and/or an input port (e.g., USB port).

The memory 131 can be, but is not limited to, for example, a memorybuffer, a random access memory (RAM), a read-only memory (ROM), a harddrive, a flash drive, a secure digital (SD) memory card, a compact disk(CD), and/or a universal flash storage (UFS) device. The memory 131 canstore, for example, data (e.g., user data 137, setting data 138, drugdata 139, etc.) and one or more codes that includes instructions tocause the processor 132 to perform one or more processes or functions(e.g., the score generator 143, the profile updater 144, thepersonalizer 148, etc.).

The memory 131 can store data including user data 137, setting data 138,and drug data 139. The user data 137 can include, but is not limited to,sensor data (e.g., EEG data, respiratory rate data, etc.) of the userreceived from the sensor 120, biographic data of the user, demographicdata of the user, and/or user profile data provided by the user and/or atherapist. In some implementations, the user data 137, the setting data138, and drug data 139 can be collected during a pre-treatment session(e.g., before a psychedelic treatment) and/or during a treatment sessionor in-treatment session (e.g., during a psychedelic treatment).

In some instances, the memory 131 can also store user data from pasttherapy sessions (e.g., to collect data for training a machine learningmodel). For example, the user data from past therapy sessions caninclude, but is not limited to, past performance data, past clinicalresponse data, past difficulty level data, past session length data,past raw sensor data, past setting response, and/or past exercise and/orexperience score data.

The drug data can include medications of the user including, but notlimited to, for example, a psychedelic drug, including, but not limitedto, for example, Psilocybin, Ketamine, Esketamine, R-Ketamine, RL-007(e.g., for schizophrenia), Ibogaine, Deuterated Etifoxine,N-Acetylcysteine, methylenedioxy-methylamphetamine (MDMA),N-methyl-1-(3,4-methylenedioxyphenyl)propan-2-amine),methylenedioxy-methylamfetamine, 3,4-methylenedioxymethamphetamine,3,4-Methylenedioxyamphetamine (MDA), Salvinorin A, DeuteratedMitragynine, Noribogaine, Dimethyltryptamine (DMT), N,N-DMT,D-Cycloserine, psychedelics, antidepressants, fluoxetine, sertraline,paroxetine, citalopram, venlafaxine, benzodiazepines, valproate, lithiumcarbamazepine, tiagabine, buspirone, barbiturates, diltiazem, or otherdrugs with acute central nervous system effects. The drug data canfurther include medication dosage data of the user and/or drugconsumption timeline data of the user. The setting data can include, butis not limited to, multi-sensory setting data provided by the user,multi-sensory setting data provided by a therapist, and/or setting datafrom a digital therapy or intervention.

The communication interface 133 of the compute device 130 can be ahardware component of the compute device 130 to facilitate datacommunication between the compute device 130 and the therapy device 110and/or the sensor(s) 120. The communication interface 133 is operativelycoupled to and used by the processor 104 and/or the memory 102. In someembodiments, the communication interface 133 can also facilitate datacommunication between the compute device 130 and an external device(e.g., a server; not shown). The communication interface 133 can be, forexample, a network interface card (NIC), a Wi-Fi® transceiver, aBluetooth® transceiver, an optical communication module, and/or anyother suitable wired and/or wireless communication interface. Forexample, the communication interface 133 can facilitate receiving ortransmitting the user data 137, the setting data 138, the drug data 139,a machine learning model(s), and/or the like, from/to the server (notshown).

The processor 132 can be, for example, a hardware based integratedcircuit (IC) or any other suitable processing device configured to runor execute the one or more codes. For example, the processor 132 caninclude, but is not limited to, a general-purpose processor, a centralprocessing unit (CPU), an accelerated processing unit (APU), anapplication specific integrated circuit (ASIC), a graphics processingunit (GPU), and/or a deep learning processor. In some instances, theprocessor 132 can be operatively coupled to the memory 131 through asystem bus (for example, address bus, data bus, and/or control bus, notshown). The processor 132 includes a sensor data evaluator 142, a scoregenerator 143, a profile updater 144, a configurator 145, a presenter147, and a personalizer 148 that can be or include software stored inthe memory 131 and executed by the processor 132. For example, code tocause the score generator 143 to determine a measure associated with astate of a user can be stored in the memory 131 and executed by theprocessor 132. Alternatively, the sensor data evaluator 142, the scoregenerator 143, the profile updater 144, the configurator 145, thepresenter 147, and the personalizer 148 can include hardware-baseddevices. For example, a process to cause the presenter 147 to show aprogress of meditation, attention, and/or relaxation states of the usercan be implemented on an individual integrated circuit chip.

As shown in FIG. 1 , the compute device 130 can be or include a localcompute device (e.g., a desktop computer, a laptop, a mobile phone, achip/processing unit, or a tablet) that is connected (e.g., using awire(s)) and/or is operatively coupled (e.g., via electromagnetic waves)to the therapy device 110 and the sensor(s) 120. Although shown asseparate devices, in some embodiments, the compute device 130, thetherapy device 110, and/or the sensor(s) 120 can be implemented in anintegrated device can collectively perform functions of the computedevice 130, the therapy device 110, and/or the sensor(s) 120. Forexample, in some embodiments, the integrated device can be/include aheadset (e.g., a virtual reality headset or an artificial realityheadset) that can present a digital therapy during a pre-treatmentsession and/or during an in-treatment session, sense data from the userduring the pre-treatment session and/or during the in-treatment session,and process the data to adaptively adjust/improve digital therapy, asdescribed in further details herein.

In some embodiments the electronic circuitry, function, modules, models,and/or codes of the compute device 130 can be implemented on a server(e.g., a storage database, a cloud computing server). The server canbe/include one or more compute devices particularly suitable for datastorage, data processing, and/or data communication. For example, theserver can include a network of electronic memories, a network ofmagnetic memories, a server(s), a blade server(s), a storage areanetwork(s), a network attached storage(s), deep learning computingservers, deep learning storage servers, and/or the like. The server caninclude a memory, a processor, and/or a communication interface that arestructurally and/or functionally similar to the memory 131, theprocessor 132, and/or the communication interface 133, as shown anddescribed with respect to the compute device 130. In one example, theuser data, the setting data, and/or the drug data can be stored on adatabase server. In another example, a machine learning model of thescore generator 143 can be trained and/or executed by one or moregraphical processing units of a computing server (not shown in FIG. 1 ).

The sensor data evaluator 142 can evaluate or determine (e.g., determinea measure of data sufficiency) whether user data 137 received from thesensor 120 and stored in the memory 131 includes sufficient informationfor determining a relaxation state (e.g., based on EEG data and/or HRVdata), an attention state (e.g., based on EEG signals from thefrontal/prefrontal regions), a meditation state (e.g., based on EEG dataand/or HRV data), or other suitable state of the user, e.g., forassessing whether the user is prepared for receiving a treatment (e.g.,a psychedelic). Similarly stated, the sensor data evaluator 142 can beused as a filter to determine whether the user data 137 provided by thesensor 120 and/or stored in the memory 131 is suitable (e.g., has asignal-to-noise ratio (SNR) above a predetermined threshold) fordetermining an indication of state (e.g., including the relaxationstate, the attention state, the meditation state, or another state ofthe user).

In some instances, for example, the sensor 120 can provideelectroencephalography (EEG) data to the compute device 130 and/or storethe EEG data in the memory 131. The sensor data evaluator 142 canperform a substantially real-time impedance measurement(s) and determinewhether specific impedance levels meet previously determined criteria(e.g., threshold impedance values being below 1 mΩ, 1 Ω, 10 Ω, 100 Ω, 1KΩ, 10 KΩ20 KΩ, or 1 MG). For example, a noise to EEG signals can becaused by, but is not limited to, muscle movements, blinking, sweat,unintentional electrostatics, that can interfere with the EEG signalsand making the EEG data less accurate. In some implementations, thesensor data evaluator can determine if a signal-to-noise (SNR) ratio ofthe EEG data is not suitable for further processing. When the sensordata evaluator 142 determines that the user data provided by the sensor120 and/or stored in the memory 131 is not suitable and/or sufficientfor determining an indication of state, the processor 132 can generate anotice to the user and/or a therapist, which can, for example, instructthe user to correct or address the unsuitability of the data and/orpropose a remedial action (e.g., to adjust a position of a sensor on thebody of the user). On the other hand, when the sensor data evaluator 142determines that the user data 137 provided by the sensor 120 and/orstored in the memory 131 is suitable and/or sufficient for determiningan indication of state, the user data 137 can be passed on to the scoregenerator 143.

In some implementations, the sensor data can include heart ratevariability (HRV) data and/or breathing data. The HRV data can becollected, for example, using a photoplethysmography (PPG) sensor builtinto a BCI interface. The sensor data evaluator 142 can evaluate the HRVdata and/or the breathing data, and the score generator 143 can generatethe score based on the HRV data and/or the breathing data. A feedbackbased on the HRV data can generate a balance between the sympatheticnervous system (SNS) and the parasympathetic nervous system (PNS), whoseimbalance (e.g., hyperarousal of the SNS, disrupted the PNS function) isassociated with higher levels of anxiety. HRV reflects the relationshipbetween the PNS and the SNS and can be sensitive to changes in emotionalstate, especially with regard to valence. As such, HRV biofeedback canbe used to assist the user with self-regulating the user's emotion(s) byincentivizing the user to enhance cardiac coherence and create autonomicbalance.

In some implementations, the sensor data can include EEG data combinedwith the HRV data. The sensor data evaluator 142 can evaluate the EEGdata and the HRV data, and the score generator 143 can generate thescore based on the EEG data and the HRV data. In an embodiment, the EEGdata and the HRV data can be used by the score generator 143 to generatea score, as further described in the sections below.

In some implementations, the sensor data can include blood glucose datacombined with HRV data as measured by various sensors. The sensor dataevaluator 142 can evaluate the blood glucose data and the HRV data, andthe score generator 143 can generate the score based on the bloodglucose data and the HRV data. In an embodiment, the blood glucose dataand the HRV data can be used by the score generator 143 to generate ascore, as further described in the sections below.

In some implementations, the sensor data can include EEG data combinedwith respiratory rate data. The sensor data evaluator 142 can evaluatethe EEG data and the respiratory rate data, and the score generator 143can generate the score based on the EEG data and the respiratory ratedata. In an embodiment, the EEG data and the respiratory rate data canbe used by the score generator 143 to generate a score, as furtherdescribed in the sections below.

In some implementations, the sensor data can include galvanic skinresponse data combined with HR variability data. The sensor dataevaluator 142 can evaluate the galvanic skin response data and the HRvariability data, and the score generator 143 can generate the scorebased on the galvanic skin response data and the HR variability data. Inan embodiment, the galvanic skin response data and the HR variabilitydata can be used by the score generator 143 to generate a score, asfurther described in the sections below.

In some implementations, the sensor data can include EEG data, HRV data,GSR data, glucose data, pupillometry data, or combination thereof. Thesensor data evaluator 142 can evaluate the EEG data, the HRV data, theGSR data, the glucose data, and/or the pupillometry data, and the scoregenerator 143 can generate the score based on the EEG data, the HRVdata, the GSR data, the glucose data, the pupillometry data, orcombination thereof. In an embodiment, the EEG data, HRV data, GSR data,glucose data, pupillometry data, or combination thereof can be used bythe score generator 143 to generate a score, as further described in thesections below.

The score generator 143 can be executed to receive the user data 137,the setting data 138 and/or drug data 139 and to generate using thatdata the indication of a state of the user (e.g., indicative of arelaxation state of the user, an attention state of the user, ameditation state of the user, a state of user related to the user'scentral nervous system (CNS), valence, and/or any other appropriatemental state of the user for psychedelics). In some implementations, theindication of state can be used to determine a measure of progression ofthe user, indicating whether the user is progressing throughmulti-sensory experience or exercise (e.g., a meditation program, agameplay, an adaptive settings system, a digital therapy, a relaxationtherapy/training, an attention therapy/training, and/or a meditationtherapy/training including one or more sensory elements such as visual,audio, tactile, etc.) or other digital content and/or programming. Themeasure of progression can be defined as a measure of whether a user isprogressing towards the goal in any given moment and how well they areprogressing in that moment. For example, in some instances, amulti-sensory experience or exercise can include at least one end-goal(e.g., a pattern/shape) and the user can actively try to achieve thatend-goal within the multi-sensory experience or exercise itself (e.g.,turn scattered particles into the pattern/shape). In someimplementations, the indication of state can be used to determine ameasure of completeness of the user. The measure of completeness can bedefined as a measure of percentage completed in a level or session ofthe multi-sensory experience or exercise. The measure of progression canbe, for example, equivalent to a measure of whether a user isprogressing towards the goal at a given period of time and/or how wellthey are progressing in that moment. In some implementations, theindication of state can be used to determine a measure of response tothe multi-sensory experience or exercise (e.g., one or more digitalsettings) or, in other words, a measure of progression toward optimalmental state or set in response to the multi-sensory experience orexercise. For example, in some instances, the multi-sensory experienceor exercise has no beginning or end-goal, and/or the user can be passiveparticipant. In other words, in some instances, there is no indicationof progression within the multi-sensory experience or exercise.Therefore, progress in this context can be a measure of mental stateresponse to a digital setting and not a measure of how far a gameplay,content, or exercise has progressed.

In one example, a multi-sensory experience and/or exercise can include agameplay. The gameplay can have one or more levels or checkpoints. Eachlevel of the gameplay can initially have a difficulty assigned to thatlevel, which can be, for example, represented by a preset thresholdscore. A level of difficulty/threshold score within the level canpotentially change based on the adaptive difficulty system, butotherwise it can stay substantially the same for the entirety of thatlevel of difficulty/threshold score. The threshold score can be used todetermine the minimum score a user needs to achieve in order to beprogressing towards an end state/goal in the level of difficulty. Thehigher the user score is above the threshold, the faster the user canprogress in the level of difficulty and/or towards the end state/goal.If the user falls below the threshold, the user can regress away fromthe end state/goal and/or the level of difficulty, or can make slowerprogress towards the end goal. In another example, the profile updater144 can receive user data (e.g., collected from the sensor(s) 120) andcan update a subject profile based on a mental state response(s) of theuser to various digital settings presented to user. In other words, theprofile updater 144 can update a subject's profile based on user dataindicating a set of one or more settings that induce or maintain anoptimal mental state or set for the subject. In some embodiments, athreshold score can be assigned to different settings (e.g., digitaltherapy, sensory objects or elements, etc.), with the threshold scoreindicating when it may be appropriate to change or maintain one or moreof the settings. The threshold score can be related or specific to thesubject. For example, a first subject's score can be different (lower orhigher) from a second subject's score to indicate that a setting works.In other words, the threshold score can be lower for the first subjectto accommodate for a lower score indicating positive outcome of thesetting versus the same score not indicating a positive outcome in thesecond subject. The configurator 145 can then adapt a possiblesetting(s) that can be presented to the user based on the user's updateduser profile or threshold score(s). In yet another example, a thresholdscore can be adapted for each user for refining an adaptive setting(e.g., to determine to change or maintain a current digital setting).

In some implementations, each level of difficulty can have a beginningstate and end state/goal (e.g., defined by the visuals and audio). Thescore generator 143 can generate a set of scores for the user at presetintervals (e.g., every 100 ms). When a score from the set of scores,e.g., generated for a certain period of time, is above the threshold,the user is progressing towards the end state/goal (e.g., at a ratebased on how far the user is above the threshold). When the score (e.g.,generated for a particular period of time) is below the threshold, theuser is regressing or progressing more slowly towards the beginningstate (e.g., at a rate based on how far the user is below thethreshold). To get to the end state/goal, the set of scores of the usershould be averaging above a preset threshold value for an associatedperiod of time. For example, the period of time can be determined basedon how much the set of scores of the user is averaging above thethreshold score, with higher scores having shorter times to completion(e.g., higher scores being associated with shorter periods of time).

In some instances, the score generator 143 can be adapted to calculate ameasure (e.g., score) indicative of a meditation state, an attentionstate, a relaxation state, or other mental or physiological state of theuser, e.g., using a probabilistic model (e.g., a supervised learningmodel, an unsupervised learning model, an operant learning model). Insome implementations, the score generator 143 can calculate anevaluation of the meditation state, the attention state, and/or therelaxation state of the user in response to a performance of the user(e.g., during a digital therapy) or in response to a digital settingpresented to the user. The score generator 143 can be operativelycoupled to the profile updater 144.

In some instances, the score generated by the score generator 143 can bea score between 0 and 100 representing a mental state, with 0representing intense psychological discomfort and 100 being completelycomfortable and at-ease. Alternatively, any numerical and/or other typeof scale can be used, e.g., a numerical scale from −100 to 100, a coloror gradient based scale, etc. In some embodiments, the score generatorcan include multiple probabilistic models, each bespoke to or associatedwith a specific psychedelic compound or drug treatment.

In some instances, the score generator 143 can implement a machinelearning model and can include a set of model parameters (e.g., nodes,weights, biases, etc.) that can be used to determine an indication ofstate associated with the digital therapy based on the user data 137,the setting data 138, and/or the drug data 139. The indication of statecan be indicative of a relaxation state, an attention state, ameditation state, or other mental state of the user. In one example, thescore generator 143 can generate a score between 0 and 100 with 0indicating a completely unfocused user and 100 indicating a completelyfocused user. In another example, the indication of state generated bythe machine learning model can be an array of numbers including a firstnumber indicative of a relaxation state, a second number indicative ofan attention state, and a third number indicative of a meditation state.In yet another example, the indication of state generated by the machinelearning model can be an overall indication of state that represents anaverage or a weighted average of the relaxation state, the attentionstate and the meditation state. While relaxation, attention, andmeditation states are described herein with respect to the examples, itcan be appreciated that other types of mental states and/or any numberof mental states can be used with the systems, devices, and methodsdescribed herein. And while numerical scores are described herein, itcan be appreciated that numerical scores and/or other types of scores(e.g., words, colors, etc.) can be used.

In some instances, the score generator 143 can be a supervised machinelearning model configured to receive a set of past user data (e.g., aset of past sensor data), a set of past setting data, a set of past drugdata and a set of determined indications of state. A subset of user datafrom the set of past user data, a subset of setting data from the set ofpast setting data, and/or subset of drug data from the set of past drugdata can be associated with a determined indication of state from theset of determined indications of state to produce labeled data. Thelabeled data can be used to train the set of model parameters (e.g., toidentify the set of model parameters and/or determine weights associatedwith model parameters).

The score generator 143 can include, but is not limited to, a supervisedmachine learning model, a deep learning model, a boosted decision treemethod, an ensemble of decision trees, an extreme gradient boosting(XGBoost) model, a random forest, a support vector machine (SVM), afeed-forward machine learning model, a recurrent neural network (RNN), aconvolutional neural network (CNN), a graph neural network (GNN), anadversarial network model, an instance-based training model, atransformer neural network, and/or an ensemble of machine learningmodels. The set of model parameters of the score generator 143 caninclude a set of weights, a set of biases, and/or a set of activationfunctions that, once trained, can be executed to generate an indicationof state from the user data 137, the setting data 138, and/or the drugdata 139.

In one example, the score generator 143 can be a deep learning modelthat includes an input layer, an output layer, and multiple hiddenlayers (e.g., 5 layers, 10 layers, 20 layers, 50 layers, 100 layers, 200layers, etc.). The multiple hidden layers can include normalizationlayers, fully connected layers, activation layers, convolutional layers,temporal convolutional layer, spatial convolutional layers, recurrentlayers, and/or any other layers that are suitable for representing acorrelation between the indication of state and the user data 137, thesetting data 138, and/or the drug data 139.

In one example, the score generator 143 can be an XGBoost model thatincludes, but is not limited to, a set of hyper-parameters such as, forexample, a number of boost rounds that defines the number of boostingrounds or trees in the XGBoost model, and/or maximum depth that definesa maximum number of permitted nodes from a root of a tree of the XGBoostmodel to a leaf of the tree. The XGBoost model can include, but is notlimited to, a set of trees, a set of nodes, a set of weights, and/or aset of biases.

In some implementations, the score generator 143 (e.g., a deep learningmodel or an XGBoost model) can be configured to iteratively receive asubset of user data from the set of past user data, a subset of settingdata from the set of past setting data, and/or a subset of drug datafrom the set of past drug data described above and generate an output.Each subset of user data from the set of past user data, each subset ofsetting data from the set of past setting data, and/or each subset ofdrug data from the set of past drug data is associated with a determinedindication of state from the set of determined indications of state. Theoutput and the determined indications of state can be compared using anobjective function (also referred to as cost function) to generate atraining loss value.

The objective function can include, but is not limited to, for example,a mean square error objective function, a mean absolute error objectivefunction, a mean absolute percentage error objective function, a logcoshobjective function, and/or a categorical crossentropy objectivefunction. The set of model parameters of the score generator 143 can bemodified or optimized using an optimization method (e.g., a gradientdescent method, a stochastic gradient descent method, the Adagradmethod, the Adam method, and/or the Adadelta method) in multipleiterations. The objective function can be executed at each iterationfrom the multiple iterations until the training loss value converges toa predetermined training threshold (e.g., 80%, 85%, 90%, 95%, 99%,etc.). In some instances, the predetermined training threshold can bemanually determined/set. In some instances, the predetermined trainingthreshold can be automatically set based on a training time for thescore generator 143.

Once trained, the score generator 143 can be executed to generate theindication of state from the user data 137, the setting data 138, and/orthe drug data 139 within an accuracy margin defined by the predeterminedtraining threshold. In some implementations, the compute device 130 canoptionally include an auditor (not shown) that can verify the indicationof state for the user data 137, the setting data 138, and/or the drugdata 139 to generate (e.g., by prompting a physician or a therapist todetermine a state from the user data 137, the setting data 138, and/orthe drug data 139) a truth-value state and an accuracy score of thescore generator 143. The score generator 143 can be further trainedusing the accuracy score, the user data 137, the setting data 138,and/or the drug data 139.

Once trained, the score generator 143 can generate the indication ofstate from the user data 137, the setting data 138, and/or the drug data139. In some instances, for example, the score generator 143 cangenerate an indication of state of a user in a fraction of the time itnormally would take by, for example, a therapist to determine a scorebased on the user data 137, the setting data 138, and/or the drug data139. In some instances, a therapist can generally spend, for example, 1minute, 2 minutes, or 5 minutes, to generate the indication of statebased on the user data 137, the setting data 138, and/or the drug data139. In contrast, the score generator 143 can spend, for example, 100milliseconds, 1 second, or 2 seconds to generate the indication of statebased on the user data 137, the setting data 138, and/or the drug data139. Therefore, the score generator 143 can be used to generate a set ofindications of state from setting data, user data, and/or drug dataquickly to substantially improve determination of state of users (e.g.,10 times faster, 100 times faster, 1000 times faster, 1,000,000 timesfaster, or 1,000,000,000 times faster). In some instances, the scoregenerator 143 can generate an indication of state of a user that moreaccurately or objectively reflects a state of the user than adetermination of state made by a therapist based on the user data 137,the setting data 138, and/or the drug data 139. This can be due to, forexample, personal biases of a therapist, inexperience of a therapist, orother factors. The score generator 143 can be configured to account forsuch biases and provide scores indicative of a user's state that areobjective measures.

The profile updater 144 can receive the indication of state of the user(e.g., indicative of a relaxation state, an attention state, ameditation state, or other mental state of the user) from the scoregenerator 143. In response to receiving the indication of state of theuser, the profile updater 144 can update a user profile of the user(e.g., stored in the memory 131) to generate an updated user profile.The profile updater 144 can be operatively coupled to the configurator145 that can set a difficulty level of the multi-sensory experience orexercise (e.g., during a digital therapy and/or intervention) or updatea setting selection, based on the user's performance during themulti-sensory experience or exercise and/or in response to the updateduser profile of the user. Alternatively, in some instances, theconfigurator 145 can be manually set by a professional (e.g., atherapist or a physician) to set the difficulty level of themulti-sensory experience or exercise based on the indication of state.The configurator 145 can set a difficulty of the multi-sensoryexperience or exercise for each user based on previous assessments ofrelaxation, attention, meditation and/or other states as determined bythe sensor data evaluator 142. In some embodiments, setting thedifficulty level can include establishing a higher threshold that ascore of the user must reach and maintain, e.g., to successfully advancepass a checkpoint or level.

The personalizer 148 can receive, for example, the user data 137 (e.g.,bio-signals), the setting data 138 (e.g., an indication of a musicpreference), and/or the drug data 139 (e.g., an indication drugconsumption amount) of the user during a multi-sensory experience orexercise and the machine learning model of the score generator 143 (alsoreferred to as the “global classifier” or the “classifier”) to produce auser-specific score model (e.g., a user-specific classifier, as furtherdescribed with reference to FIG. 3 ). For example, the user data 137,the setting data 138, and/or the drug data 139 of the user during amulti-sensory experience or exercise can be evaluated, e.g., by acompute device or a professional (e.g., a therapist or a physician),over a time period (e.g., five minutes, ten minutes, twenty minutes,five hours, ten hours, twenty hours, one day two days, or one week, twoweeks, etc.) to produce truth-value indications of state of the user.The truth-value indications of state of the user, the user data, thesetting data, the drug data, and/or past score data can be used toretrain the machine learning model of the score generator 143 andproduce the user-specific score model. Doing so can provide a furtherpersonalized training method for the user in accordance to the conditionand unique signals of the user. The personalizer 148 can control andadapt the compute device 130 to the specific needs and/or uniqueconditions of each individual user.

In some instances, the compute device 130 can be configured to track aprogress of the user and/or an adherence of the user to one or moremulti-sensory experience or exercise sessions and/or goals. In someinstances, the compute device 130 can collect metadata (e.g., timestampsof events and/or collected data) during the one or more digital therapysessions. For example, a timestamped set of data of the user duringtherapy exercise conducted during a time (e.g., a week, 2 weeks, amonth, 2 months, etc.) can be plotted to show a trend of the progress ofthe user and/or adherence of the user to the one or more multi-sensoryexperience or exercise sessions.

In some implementations, the processor 132 can optionally execute adifficulty model (not shown) that can adaptively advance, regress, orchange the multi-sensory experience or exercise including, but notlimited to, a relaxation therapy/training exercise, an attentiontherapy/training exercise, a meditation therapy/training exercise,and/or any other suitable experiences or exercises administered by thetherapy device 110, based on a performance of the user. In someinstances, for example, when the score generator 143 determines, duringa multi-sensory experience or exercise, that the user data 137 (e.g.,sensor data), the setting data 138, and/or drug data 139 of a user areindicative of meeting a criterion (e.g., progressing towards the goal ormoving away from the goal), the difficulty model can advance, regress,or change the multi-sensory experience or exercise for the user. In oneexample, the difficulty model can change a threshold score to advance orregress in a gameplay of the multi-sensory experience or exercise basedon the indication of state (also referred to as the “score”) of theuser, calculated by the score generator 143. In some implementations,the processor 132 can optionally execute a setting selection model (notshown) that can adaptively select a setting(s) and subsequently update,via the profile updater 144, a subject profile based on a mental stateresponse(s) of the user to various digital settings presented to user.

The presenter 147 of the compute device 130 can be operatively coupledto an auditory interface, a visual interface, an olfactory interface, agustatory interface, and/or a haptic interface. In some embodiments, thepresenter 147 can be operatively coupled to the therapy device 110,which can include an auditory interface, a visual interface, anolfactory interface, a gustatory interface, and/or a haptic interface.In some implementations, the presenter 147 can be configured to present,using the therapy device 110 and/or other sensory element presentingdevice, a scene including one or more objects to a user. The one or moreobjects can be, for example, one or more visual, olfactory, gustatory,auditory, or haptic signals or elements. In some implementations, thepresenter 147 can show a progress of meditation, attention, and/orrelaxation states of the user (e.g., by providing feedback). Forexample, the presenter 147 can be coupled to a virtual realityinterface, an augmented reality interface, a visual display interface, aprojection system interface, a speaker/headphones interface, or a hapticinterface that show/convey a progression of the user to the user. Inaddition, or alternatively, in some implementations, the presenter canprovide a guidance to the user, such as to feed-forward, e.g., todisplay arrow(s) or other instructions to drive or advance a state of agameplay (or other experience or exercise) toward an end-goal. In someimplementations, however, the presenter 147 can be configured to besilent (e.g., about feedback or feedforward to the user) so that thetherapy system can adapt to the user in a way that changes the user'smental state whether the user is actively aware of the user's mentalstate or not.

The therapy device 110 (also referred to as the “training device”) caninclude, but is not limited to, a virtual reality or VR device (e.g.,Oculus VR™, HoloLens™, Muse™ 2016 brain-computer interface (BCI) device,HP Reverb G2™, etc.), an augmented device, a projection system (e.g.,including a projector and a screen), a display device (e.g., atelevision screen), a display system (e.g., aggregated monitorsconnected and managed by a controller), an auditory device (e.g., aspeaker and/or a headphone), an auditory system (e.g., an immersiveaudio and/or binaural sound system including multiple speakers andcontrolled by a controller), a haptic device, a olfactory virtualreality device(s), a light system(s), a strobe light(s), a tactile vest,a scent delivery system, and/or a haptic system (e.g., multiple hapticdevices for various part of the body of the user). In use, the therapydevice 110 can be modified to adapt to an appropriate setting(s), e.g.,advance to next exercise difficulty (for sequential difficulty levels),and/or adapt to an appropriate exercise difficulty (for non-sequentialdifficulty levels) based on data collected by the sensor 120 and/orinstruction generated by the compute device 130 based on that data. Insome instances, a difficulty of the exercise of the therapy device 110can stay the same but advance, for example, within a single-level orportion of the meditation game. The advancement through the samedifficulty level can be reflected, for example, in a visual changetoward a positive direction, a visual change of color, a visual changeof scenery, an auditory change of a music, and/or a change in avibration. In some embodiments, a multi-sensory experience or exercisemay not include any levels. For example, a user may advance throughdifferent portions or checkpoints of an exercise, e.g., depending on auser's ability to focus and/or relax. In some embodiments, amulti-sensory experience or exercise may last for a predetermined periodof time and conclude after that predetermined period of time, regardlessof whether a user has advanced or progressed during the experience orexercise.

The therapy device 110 can be operated by the user and can beoperatively coupled to the compute device 130 to receive data and/orinstruction from the compute device 130. For example, in some instances,the therapy device 110 can receive signals (e.g., radio frequency (RF)signals, optical signals, and/or electrical signals) from the computedevice 130 (e.g., from the score generator 143, the configurator 145,and/or the presenter 147). The signal from the compute device 130 canindicate whether the user data (received from the sensor 120) includethe information indicative of attention, relaxation, or another mentalstate. The therapy device 110 can be modified/configured not to operatewhen the signal from the compute device 130 indicates that the user isnot participating and/or ready for a therapy session (e.g., if sensordata does not contain necessary information to make a determination ofthe user's mental state). In another example, the therapy device 110 canreceive signals from the compute device 130. The signal from the computedevice 130 can also indicate at least one score in an exercise level ora progression in that exercise level.

In use, the therapy system 100 can be used in a pre-drug or proceduresession and/or a in-drug or procedure session. For example, the therapysystem 100 can be used for a psychedelic treatment before and/or during(e.g., for inducing and maintaining a desired or optimal set for a user)administration of a psychedelic drug. The therapy system 100 can runcontent such as an exercise and/or experience (e.g., including a set ofvisual content, a set of audio content, a set of haptic signals, a setof olfactory content, or a set of gustatory content) using the therapydevice 110. The sensor(s) 120 can acquire sensor data during theexercise and/or experience and using, for example, a set of electrodesto head of the user that measure physiological activities and/orbehavioral activities including, but are not limited to, EEG,electrooculography, heart rate, heart rate variability, galvanic skinresponse, respiratory rate, pulse oximetry, EKG, eye movements, facialexpressions, glucose, pupillometry, and/or the like. The sensor dataevaluator 142 of the compute device 130 can receive the sensor data fromthe sensor(s) 120 and determine whether the sensor data includesinformation indicative of optimal set functions used for developing theoptimal or a suitable set for a psychedelic experience. For example, thesensor data evaluator 142 can measure impedance of the set of electrodesconnected to the head of the user and determine whether the measuredimpedance values are within a threshold interval.

In some implementations, the processor 130 can further include anotifier (not shown) that can generate a notification (e.g., a messageand/or a signal in response to receiving sensor data from the sensor(s)120). In some instances, for example, the notifier (not shown) can senda signal to cause the compute device 130, the sensor(s) 120, and/or thetherapy device 110 to show a red light (e.g., using a light emittingdiode or displayed on the therapy device 110) to prompt user to fix aprobe (e.g., an EEG probe) in response sensor data evaluator 142indicating the sensor data provided by the sensor(s) 120 is not suitable(e.g., has an impedance outside a predetermined impedance interval) fordetermining a state of the user. In some instances, for example, thenotifier (not shown) can send a signal to cause the compute device 130and/or the therapy device 110 to show a message (e.g., generated by theprocessor 132) to provide specific guidance to the user about adjustingand/or changing a configuration of the sensor(s). For example, thenotifier can send a message to the therapy device 110 directing the userto sit and/or stand at a certain distance (e.g., a meter) from thesensor(s) 120 to reduce noise in the sensor data.

The score generator 143 can receive the sensor data to determine (e.g.,using a specialized machine learning model for a specific psychedeliccompound and/or its expected effects on treatment response) anindication of state of the user (e.g., a score) representing anassessment of the user's response to the psychedelic treatment and/or acurrent state of an adaptive setting (e.g., a set of parameters of themulti-sensory experience or exercise that can be adjusted/tuned and/or adifficulty level) of the multi-sensory experience or exercise.Therefore, in some instances, the score generator 143 can determinewhether a current state of the adaptive setting is successful ininducing or maintaining an optimal set. In some implementations, thetherapy device 110 can include a feedback system indicator to display,to the user, an indication of the user's mental state in response to thecurrent state of the adaptive setting. The feedback system indicatorcould be in a visual format, auditory format, olfactory format,gustatory format, and/or haptic format. In some implementations, thetherapy system 100 can include a feedback component(s) (e.g., modelsimplemented in the compute device 130) that could be auditory, visual,gustatory, olfactory and/or haptic to reflect back to individuals' theircurrent calculated score. In some implementations, the therapy system100 can include a feedforward reward response component(s) (e.g., modelsimplemented in the compute device 130) to guide and drive individualstowards the target mental state. In some implementations, the scoregenerator 143 can, in addition to the sensor data, be trained based onsetting data 138 (e.g., music preference), drug data 139 (e.g., drugdosage), and user data 137 (e.g., medical data received from athird-party compute device) other than the sensor data.

The compute device 130 can be configured to adaptively change ormaintain the current state of the presented sensory environment (e.g., ascene and/or objects within a scene) based on the estimated score. Insome implementations, the compute device 130 can further include anadaptive setting model that determines whether or not a sufficientscore, as determined by the score generator 143, is achieved for thecurrent state of the adaptive setting of the multi-sensory experience orexercise. When the user maintains a satisfactory score, as determined bythe score generator 143, the setting of the multi-sensory experience orexercise can either remain the same or change in a way that is conduciveto maintaining the satisfactory score. When the user does not maintain asatisfactory score, as determined by the score generator 143, theadaptive setting can be changed via changes to the multi-sensoryexperience or exercise (e.g., including a change(s) made to the set ofvisual content, the set of audio content, the set of haptic signals, theset of olfactory content, or the set of gustatory content). In someinstances, the adaptive setting model can guide or activelyinduce/maintain optimal set in the user during a digital therapy orother presented content towards a target mental state.

In some implementations, the personalizer 148 can modify/adjust thescore generator 143 to the specific needs and unique conditions of eachindividual. For example, the score generator 143 can include apersonalized machine learning model that can be developed on dataspecific to the user (e.g., sensor generated during a pre-treatmentsession(s) and/or in-treatment session(s)). For example, in someinstances, the personalizer 148 can be modified to take in thebio-signals of a user during a psychedelic experience and use this userdata to train a user-specific machine learning model from the existingscore generator 143.

The profile updater 144 can then receive an indication of state of theuser (e.g., the score) from the score generator 143 and in response tovarious states of the adaptive setting. and update a user profile of theuser to generate an updated user profile. The updated user profile canbe stored in the memory 131. In some instances, the profile updater 144can use the indication of state of the user to categorize the user. Forexample, in some instances, the profile updater 144 can search anattention score(s) or other score(s) of the user (e.g., collectedpreviously during a training exercise) and then assign the user to aspecific competency group based on the attention score(s) or otherscore(s). In some instances, the profile updater 144 can update thesubject's profile based on a setting(s) that induces or maintains anoptimal set(s) for the subject.

The configurator 145 can then update adaptive setting of the trainingexercise in response to the updated user profile generated by theprofile updater 144. In some instances, the configurator 145 can bemanually set by a professional (e.g., a physician) to modify theadaptive setting based on the score, as determined by the scoregenerator 143. Doing so can provide a personalized adaptive settingsystem for the user in accordance to conditions and/or preferences ofthe user.

In some implementations, the user data, the setting data, and/or thedrug data can be preprocessed before being processed by the computedevice 130, as described herein. Preprocessing the user data, thesetting data, and/or the drug data can include, but is not limited to,denoising, filtering, feature identification, feature extraction,removal of artifacts (e.g., from muscle movements, blinking, jawclenching, etc.), and/or removal of outliers. For example, in someinstances, the compute device 130 can preprocess the user data, thesetting data, and/or the drug data to a numerical and fixed-sizeembedding for easier processing by the score generator. In anotherexample, the sensor(s) 120 can be configured to generate a preprocesseddata before sending the sensor data to the compute device 130. Forexample, in some instances, the sensor(s) 120 can be configured toconcatenate a time record for each datum recorded by the sensor(s) 120to generate timestamped data. the timestamped data can be then sent tothe compute device 130 for further processing (e.g., for generating ascore using score generator 130).

In some instances, when the score generator 143 of the compute device130 generates an indication of state of the user, the compute device 130can determine a modification to the presented exercise or content basedon the indication of state of the user. In one example, the modificationcan include, but is not limited to, stopping the exercise or content,moving to a next level of the exercise or content, moving to a previouslevel of the exercise or content, and/or adjusting a parameter (e.g., avolume level, or a display color) of the exercise or content. In anotherexample, the modification can include a change in the setting(s) or amodification to the setting(s).

Although in some embodiments the therapy device 110, the sensor 120, andthe compute device 130 are shown and described as singular devices, insome embodiments, the therapy system 100 can include multiple therapydevices, multiple sensors, and/or multiple compute devices. For example,in some instances, the multiple sensors can include multiple types ofsensor (e.g., photo-electric sensors, cameras, acoustic sensors,heart-beat sensors, accelerometers, humidity sensors, and/orenvironmental sensors) that collect data associated with various aspectsof a user and/or an environment of the user.

In some instances, for example, the therapy system 100 can increase theuser's sense of immersion in the adaptive setting using multiple sensorsand multiple therapy devices that surround the user. Increasing theuser's sense of immersion can lead to more significant and profoundchanges in mental state and the overall psychedelic experience of theuser. Therefore, the therapy system can achieve a higher level ofinfluence and control over the overall psychedelic experience.

Although the therapy device 110 and the compute device 130 are shown asseparate devices, in some embodiments, the compute device 130 and thetherapy device can be a combined device that can perform functions ofboth the compute device 130 and the therapy device 110.

In some instances, once the therapy system 100 and/or a physiciandetermines a target mental state(s) for an optimal set and/or an optimalsetting, a metaphor(s) can be used to induce the target mental state(s)and influence behavioral outcomes. The metaphor(s) can capture arelationship between a concrete experience and cognition. In an example,the therapy system 100 can play a high tone in response to a correctbehavior and played a low tone in response to an incorrect behavior froma first group. For a second group, a low tone can be played in responseto a correct behavior and a high tone in response to an incorrectbehavior. In some instances, individuals in the first group, on average,can adopt correct behavior better than the second group. This is becauseintuitively humans tend to view a higher tone in contrast with a lowertone as the more positively reinforcing sound. Therefore, there aresensory experiences that the user can inherently link with priorexperience. In the above example, the metaphor is the link betweenhigh/low sounds and the idea of correctness/wrongness. Given the above,a setting can encapsulate an appropriate metaphor that the user canassociate with the correct mental state.

In another example, an experience of meditation is often described asone of stillness. Specifically, that the experience of meditation is onein which a practitioner goes from being inundated with perceptions,thoughts, and stimuli to one of consistency, stillness and oneness. Assuch, an appropriate metaphor employed to intuitively convey a sense ofan increasingly meditative state (a possible example of an optimalmental state), can convey a sense of moving towards stillness and awayfrom overstimulation.

In some embodiments, the compute device 130 processes can exclude one ormore of the processes shown in FIG. 1 . In other words, one or moreprocesses executed by the processor 132 can be optional. For example,the compute device 130 processes can exclude the profile updater 144,the configurator 145, and the personalizer 148. As a result, the computedevice 130 generates a score that is an estimated measure of progressionof the multi-sensory experience or exercise using a global classifiermodel. Additionally or alternatively, the compute device 130 does notautomatically update the user profile or configure a difficulty level ofa multi-sensory experience or exercise.

In some embodiments, the therapy system 100 can be modular to keep thetherapy system 100 flexible in providing a wide variety of potentialsettings from an auditory, visual, olfactory, gustatory and hapticperspective, the whole system can be modularized. Specifically, thetherapy system 100 can allows new components to be easily added in orother components to be easily removed. Modularization of the therapysystem can have a number of benefits including, but not limited to (A)improving or modifying core game mechanics to drive better outcomes, (B)improving or modifying score generator to target a wider variety ofmental states or improve accuracy for existing mental states, (C)enabling the implementation or modification of settings in addition toadding new perceptual technologies, or (D) improving optimizer andconfigurator as new insights are gained. In one example, a head-trackedparallax visual displays can be considered, instead of a 3D-projectionmapping. In another example, the processes of the compute device 130 canbe implemented at a first compute device and a second compute deviceseparate from the first compute device and that is operatively coupledto the first compute device. For example, the first compute device caninclude the sensor data evaluator 142 and the second compute device,that is separate from and operatively coupled to the first computedevice, can include the personalizer 148.

In some embodiments, the therapy system 100 can include or implement oneor more software applications, e.g., associated with one or more of thetherapy device 110, the compute device 130, and/or a therapist orhealthcare individual. For example, the therapy system 100 canimplement, via the therapy device 110 and/or compute device 130, avirtual reality (VR) meditation application, a VR game application, atherapist web application, and/or a BCI application. The BCI applicationcan be configured to host or implement a classifier (e.g., scoregenerator 143), which can be configured to determine a state of a user(e.g., attention or focus, relaxation, etc.) based on user data (e.g.,EEG data, HRV data, etc.). The VR meditation application can beconfigured to receive data from an EEG device (or other sensor(s)) andsend the data for processing (e.g., by sensor data evaluator 142). TheVR game application can be configured to render one or more scenes,objects, and/or other elements including visual, audio, haptic,gustatory, and/or tactile signals. The therapist web application can beconfigured to allow a healthcare professional or therapist to initiate,manage, and/or track a session including a multi-sensory experience orexercise.

FIG. 2 is a flow chart illustrating the flow of data between componentsof a therapy system, according to an embodiment. The components of thetherapy system, as depicted in FIG. 2 , can be structurally and/orfunctionally similar to one or more component(s) of the therapy system100 described above with reference to FIG. 1 . For example, user datacan be acquired via input devices, such as therapy device 110 and/orsensor 120.

The flow of data and/or functions associated with the therapy system 100can include presenting multi-sensory experience or exercise such as adigital therapy (e.g., a set of visual content, a set of audio content,a set of haptic signals, a set of olfactory content, or a set ofgustatory content) to a user 201 before and/or during administration ofa drug including, but not limited to, for example, a psychedelic drug,including, but not limited to, for example, Psilocybin, Ketamine,Esketamine, R-Ketamine, RL-007 (e.g., for schizophrenia), Ibogaine,Deuterated Etifoxine, N-Acetylcysteine, methylenedioxy-methylamphetamine(MDMA), N-methyl-1-(3,4-methylenedioxyphenyl)propan-2-amine),methylenedioxy-methylamfetamine, 3,4-methylenedioxymethamphetamine,3,4-Methylenedioxyamphetamine (MDA), Salvinorin A, DeuteratedMitragynine, Noribogaine, Dimethyltryptamine (DMT), N,N-DMT,D-Cycloserine, psychedelics, antidepressants, fluoxetine, sertraline,paroxetine, citalopram, venlafaxine, benzodiazepines, valproate, lithiumcarbamazepine, tiagabine, buspirone, barbiturates, diltiazem, or otherdrugs with acute central nervous system effects. The therapy system cancollect data including user data, setting data, and/or drug data fromthe user during the presentation of the multi-sensory experience orexercise. The user data can include sensor data 202 (e.g., EEG data,heart rate variability (HRV) data, heart rate data, and/or galvanic skinresponse (GSR) data) that can be collected from the user using one ormore sensors (e.g., the sensor(s) 120 shown and described in FIG. 1 )and during the presentation of the multi-sensory experience or exercise.

The user data, the setting data, and/or the drug data can bepreprocessed and/or filtered using a preprocessor 203 before furtherprocessing and/or analysis by the therapy system. For example, in someinstances, the preprocessor 203 can normalize each datum from the userdata, the setting data, and/or the drug data to a numerical andfixed-size embedding for easier processing by a machine learning model(e.g., score generator 205) of the therapy system. FIG. 6 depicts anexample of raw EEG data 600 from a user that includes artifacts. Theartifacts (e.g., artifact 602) in the EEG data can interfere with properanalysis of the EEG data and therefore determination of a scoreindicative of a state of the user. Examples of artifacts can includenoise and/or error in measuring user data. Accordingly, preprocessingcan be performed, e.g., via preprocessor 203, to remove the artifacts.FIG. 7 depicts an example of the EEG data 702 after the artifacts havebeen removed via preprocessing. The preprocessing can include, but isnot limited to, denoising, filtering, identification and/or removal ofoutliers.

Referring back to FIG. 2 , the preprocessed and/or filtered data is sentto a sensor data evaluator 204 (similar to the sensor data evaluator 142as described with respect to FIG. 1 ) to check states of data collectionand then to a score generator 205 (similar to the score generator 143 asdescribed with respect to FIG. 1 ) to generate an indication of state ofthe user. The sensor data evaluator 204 can be adapted to determinewhether the preprocessed and/or filtered data meets certainpredetermined criteria necessary for determining a relaxation state, anattention state, a meditation state, and/or other mental state, asdescribed above with respect to FIG. 1 . For example, as depicted inFIG. 8 , EEG data 800 can be determined to not meet certain criteria,e.g., by the sensor data evaluator 204. In particular, the sensor dataevaluator can determine that the EEG data 800 contains a degree of noisethat make the data unsuitable for determining a user's state.

The sensor data evaluator 204 can send the preprocessed and/or filtereddata approved by the sensor data evaluator 204 to the score generator205. The score generator 205 can include a probabilistic model (e.g.,the machine learning model of the score generator 143 described abovewith respect to FIG. 1 ) to determine an indication of state of the userand/or whether a portion of the exercise is successfully completed.

A presenter or feedback indicator 206 (e.g., similar to the presenter147 shown and described with respect to FIG. 1 ) can be provided toillustrate to the user his or her progress in completing a portion ofthe multi-sensory experience or exercise. In some instances, thepresenter or feedback indicator 206 can be provided to illustrate to theuser his or her progress in mental state. This indicator can be providedas, for example, a visual, auditory, olfactory, gustatory, and/or hapticsignal that can be presented to the user via feedback/feedforward system209 of the therapy device (e.g., the therapy device 110 as shown anddescribed with respect to FIG. 1 ). In some implementations, thepresenter 206 can be configured to be silent (e.g., about feedback orfeedforward to the user) so that the therapy system can adapt to theuser in a way that changes the user's mental state whether the user isactively aware of the user's mental state or not.

The score generator 205 can be coupled to a profile updater 207, asdescribed above with respect to FIG. 1 . For example, the profileupdater 207 is adapted to receive the generated indication of state ofthe user (including an indication of meditation state, an indication ofattention state, an indication of relaxation state, and/or an indicationof another mental state) determined by the score generator 205, andupdate a user profile of the user consequently in response to theindication of state. The profile updater 207 is operatively coupled to aconfigurator 208 that can automatically set a difficulty level of amulti-sensory exercise (e.g., a feedback-based meditation) or optimizesetting selection/generation of one or more settings in response to theupdated user profile as determined by the profile updater 207.Alternatively, in some instances, the configurator 208 can be manuallyoperated by a professional to modify the difficulty level of themulti-sensory exercise or variables related to the selection/generationof settings.

Optionally, when being implemented with a multi-sensory exercise (e.g.,prior to a drug treatment or session such as a psychedelic experience),a difficulty model 210 (also referred to as the “adaptive difficultymodel”) can be optionally coupled to the configurator 208 to advance,regress, or change a digital therapy exercise such as, for example, arelaxation therapy/training exercise, an attention therapy/trainingexercise, and/or a meditation therapy/training exercise, based on aperformance of the user. In one example, when the score generator 205determines, using user data 137 (e.g., sensor data), and optionally thesetting data 138 and/or drug data 139, that the user is performing anexercise with a first difficulty level during a time interval shorterthan a predetermined time interval, the difficulty model 210 can advancea gameplay of the exercise with the first difficulty level for the userto a second difficulty level higher than the first difficulty level. Inanother example, the difficulty model can regress or change the gameplayof the exercise based on the indication of state of the user (e.g., ascore below a predetermined threshold) and/or a duration (e.g., thefirst 5 minutes or the first 10 minutes) of the gameplay, calculated bythe score generator 143. Therefore, the difficulty model 210 can, forexample, classify the user in a competency group from a set ofcompetency groups (e.g., beginner, intermediate, advanced, etc.). A userin a lower/higher competency segment has lower/higher preset thresholdsfor completing difficulty levels, thus making it easier/more difficultto complete a difficulty level based on the user's level of competency.In some instances, the threshold score(s) are not explicitly shown toplayers so that the user is not be aware of what competency group theyfall into and therefore there is no risk in making the user feelnegative about competency.

In some embodiments, the configurator 208 can be configured to change ormaintain one or more settings (e.g., of a digital therapy session) basedon user data, e.g., collected by the sensor(s). The configurator 208 canbe configured to change or maintain the setting(s) based on whether oneor more of the settings induce or effect a certain state in the user,e.g., induce or increase an optimal state of the user. In someembodiments, the configurator 208 can be configured to change a settingin response to determining that a setting is not conducive to achievingan optimal state of the user. For example, the configurator 208 can beconfigured to change a visual setting, an audio setting, an olfactorysetting, a tactile setting, etc. With visual settings or elements, theconfigurator 208 can be configured to change a position, color, shape,or other configuration of the visual setting or element. With audioelements, the configuration 208 can be configured to change anintensity, frequency, tone, volume, melody, rhythm, chords, or othercharacteristic of the audio setting or element. In some embodiments, theconfigurator 208 can be configured to maintain one or more settingswhile changing one or more other settings. In some embodiments, theconfigurator 208 can be configured to change settings based on whether amental state or response of the user to a digital setting has reached athreshold score or value, e.g., indicative of achieving a desirabledegree of a certain mental state. In some embodiments, the configurator208 can be configured to change or maintain the digital settings duringa drug session (e.g., a session for receiving a drug treatment such as apsychedelic).

In some implementations, the therapy system can further include apersonalizer that can be configured to take in the bio-signals of theuser during a multi-sensory experience and/or exercise session and usethis user data to develop a personalized score model from an existingglobal score model (e.g., a pre-trained machine learning model). Thiscan further provide a personalized training method for the user inaccordance with the condition and unique signals of the user.

FIG. 3 is a flow chart illustrating the flow of data between componentsof a therapy system to produce a personalized classifier, according toan embodiment. The components of the therapy system, as depicted in FIG.3 , can be structurally and/or functionally similar to one or morecomponent(s) described above with reference to FIG. 1 . The flow of dataand/or functions associated with the therapy system can includepresenting a multi-sensory experience or exercise (e.g., such as adigital therapy) to a user 301 (e.g., before administration of a drugsuch as ibogaine or during administration of a drug such as ibogaine).The therapy system can collect user data including sensor data 302(e.g., EEG data, heart rate variability (HRV) data, etc.) that can becollected from the user using one or more sensors (e.g., the sensor(s)120 shown and described in FIG. 1 ).

Past user data can be collected during a first time period (e.g., aweek, a month, or a year) from multiple users and be used to train aglobal score model 303. In some instances, the global score model 303can be similar to the machine learning model of the score generator 143.For example, the global score model can include a deep neural networkthat is trained on the past user data. The global score model 303 can beexecuted to receive the user data not among the past user data andgenerate a first indication of state of the user with a first accuracy.In some instances, the first indication of state of the user candetermine a progression of a user through a multi-sensory exercise(e.g., when a relaxation level, an attention level, and a meditationlevel of the user indicates 90% of portion or level of an exercise iscomplete).

A personalizer 305 can collect first user data associated with aspecific user (or group of users sharing similar characteristics) duringa second time period (e.g., after the first time period and training theglobal machine learning model). The personalizer 305 can then train apersonalized score model 304 (also referred to as the “user-specificmachine learning model”) that is tailored to determining a state of theuser. The personalized score model 304 can be executed to receive seconduser data (e.g., not among the first user data or the past use data) andgenerate a second indication of state of the user with a second accuracylarger than the first accuracy of the first indication of state of theuser. For example, the first accuracy can indicate that the firstindication of state of the user is about 70 percent accurate and thesecond accuracy can indicate that the second indication of state of theuser is about 90 percent accurate which is larger than the firstaccuracy.

In some implementations, the personalized score model 304 can have thesame or substantially similar model structure (e.g., neural networkstructure such as, for example, same number of layers, nodes, etc., butwith different hyperparameter values) as the global score model 303. Forexample, in some instances, the global score model 303 can include afirst neural network model with n dense layers, m temporal convolutionallayers, o spatial convolutional layers, and p drop out layers, and userectified linear unit (ReLU) activation functions, that are trainedusing past user data to generate a trained global score model. Thepersonalized score model 304 can also include a second neural networkmodel with n dense layers, m temporal convolutional layers, o spatialconvolutional layers, and p drop out layers, and use rectified linearunit (ReLU) activation functions, that uses hyperparameters (e.g.,weights, biases, etc.) from the trained global score model and the firstuser data to train the personalized score model 304. As a result, atrained personalized score model is produced that is structurallysimilar to the trained global score model but trained on additional data(e.g., associated with a specific user or group of users) and withdifferent hyperparameters (e.g., weights, biases, etc.).

In some implementations, the personalized score model 304 can have adifferent model structure (e.g., neural network structure (e.g., layers,nodes, etc.)) as the global score model 303. Returning to the aboveexample, in some instances, the global score model 303 can include aneural network model with n dense layers, m temporal convolutionallayers, o spatial convolutional layers, and p drop out layers, and userectified linear unit (ReLU) activation functions, that are trainedusing past user data to generate a trained global score model. Thepersonalized score model 304 can instead include a decision tree that istrained on the first user data to produce the trained personalized scoremodel. As a result, the personalized score model 304 is structurallydifferent from the global score model 303. Alternatively, in someimplementations, the personalized score model 304 can have a similarstructure as the global score model 303 but be configured to normalizean output from the global score model 303 based on the first user datato produce personalized scores for the user.

FIG. 4 is a flow chart illustrating a method 400 of an adaptive digitaltherapy for use with a drug treatment, according to an embodiment. Insome embodiments, the method 400 can be performed by a therapy systemthat is structurally and/or functionally similar to the therapy system100 as shown and described with respect to FIG. 1 , and/or includeelements that are similar to those described with respect to FIGS. 2 and3 . At 402, a digital therapy is presented to a user (e.g., before orduring an acute experience and/or administering the drug). The digitaltherapy can be presented to a user via a therapy device similar to thetherapy device 110 as shown and described with respect to FIG. 1 . At404, sensor data associated with the user is received in response to thedigital therapy and from at least one sensor. The at least one sensorcan be configured to measure, for example, electroencephalography (EEG),electrooculography, pulse oximetry, electrocardiogram (EKG), respiratoryrate, eye movements, pupillometry, glucose, and/or galvanic skinresponse.

At 406, the sensor data can be optionally preprocessed. For example, insome instances, the therapy system can normalize the sensor data to acommon scale, and/or a common format, for a streamline processing of thesensor data. At 408, it can be optionally determined whether the sensordata (the sensor data as received from the sensor or the preprocessedsensor data) is sufficient for evaluating a state of the user. Forexample, in some instances, the therapy system can determine whether anabsolute value of the sensor data has an amplitude above a predeterminedamplitude threshold, has a signal-to-noise ratio (SNR) above apredetermined SNR ratio, and/or is measured over a time interval longerthan a predetermined threshold. In some instances, when the sensor datais indicated as insufficient for evaluating a state of the user, thetherapy system can continue presenting the digital therapy to the user.In some instances, when the sensor data has been indicated asinsufficient for evaluating a state of the user, the therapy system canpause the digital therapy until a correction(s) are made. When thesensor data is sufficient for evaluating a state of the user, thetherapy system can then execute a score generator (e.g., similar to thescore generator 143 as shown and described with respect to FIG. 1 ).

At 410, the score generator (e.g., a machine learning model) is executedto determine a score indicative of the state of the user based on thesensor data. The score generator can receive the sensor data (or thepreprocessed sensor data) and perform, for example, a set of arithmeticoperations and/or logical operations, determined by a set of parameterof the score generator, on the sensor data to determine the score. Insome instances, the score generator can also generate a confidence scorefor the score generated. At 412, the indication of state of the user canbe assessed (e.g., be compared to a predetermined threshold value) todetermine whether the digital therapy is complete or progressing. Insome instances, the indication of the state of the user together withthe confidence score for the indication of state of the user can be usedto determine whether the digital therapy is complete. In someimplementations, the indication of state can be assessed to determine ameasure of progression toward optimal mental state in response to thedigital therapy. For example, in some instances, the digital therapy canhave no indication of progression within the digital therapy. Therefore,progress in this context can be a measure of mental state response to adigital setting.

In some instances, when a percentage value of a difference between anumerical value representing the indication of state of the user and thepredetermined threshold value is less than a preset limit such as, forexample, 5%, 4%, 3%, 2%, or 1%, the therapy exercise can be deemedcomplete and the digital therapy can be stopped (at 414). In anotherexample, when the percentage value of the difference is above the presetlimit, the digital therapy can continue with a modification to thedigital therapy based on the state (at 416). In yet another example,while the percentage value of the difference between the numerical valuerepresenting the indication of state of the user and the predeterminedthreshold value is less than 5%, the confidence score of the indicationof state can be low (e.g., 50%, 40%, 30%, 20%, or 10% confidence on theestimated score) and therefore the digital therapy can continue. In someinstances, when the confidence level of the indication of state of theuser is low, the therapy system can notify an administrator, atherapist, and/or a physician, for example, to manually determine astate of the digital therapy or confirm safety of the user under thedigital therapy.

At 416, a modification to the digital therapy can be determined based onthe indication of the state of the user. As a result, a signalindicating the modification in a presentation of the digital therapy canbe sent to the therapy device. The modification can include, but is notlimited to, stopping the digital therapy, moving to a next level ofdigital therapy, moving to a previous level of digital therapy, and/oradjusting a parameter (e.g., a volume level or a display color) of thedigital therapy.

FIGS. 9A and 9B are flow charts illustrating a method or process 900 ofimplementing a feedback-based meditation program (e.g., a multi-sensoryexercise or digital therapy), according to embodiments. In someembodiments, the method 400 can be performed by a therapy system that isstructurally and/or functionally similar to the therapy system 100, asshown and described with respect to FIG. 1 , and/or include elementsthat are similar to those described with respect to FIGS. 2 and 3 .

In some embodiments, a feedback-based meditation program can beperformed, e.g., via a gameplay. A specific example of a gameplay forpreparing a user to have an optimal set or suitable mindset forreceiving a psychedelic is described below under the heading “Example1.” The feedback-based meditation program can be performed, for example,prior to a user receiving a drug treatment, undergoing a medicalprocedure or medical exam, and/or experiencing a condition or settingwithout a drug (e.g., pain, anxiety, etc.).

In an example embodiment, a user can visit a clinic, e.g., for receivinga drug treatment or therapy. The user can undergo a pre-treatmentsession involving a feedback-based meditation program. In someembodiments, the user may be instructed to wear a BCI device, EEGdevice, and/or a PPG device. The BCI device, EEG device, and/or PPGdevice can be an example of a sensor 120. At 902, an impedance check canoptionally be performed, e.g., to confirm that the BCI device or EEGdevice has been properly placed on the user. For example, one or moreelectrodes of the BCI device or EEG device can be activated to measuresignals across those electrodes. The signals measured by thoseelectrodes can be sent to a compute device (e.g., compute device 130),which can process the signals to determine whether their values fallwithin a predefined range that is indicative of proper placement of theBCI device or EEG device. In some embodiments, if the value of anysignal falls outside of the predefined range, then the compute devicecan be configured to alert a healthcare professional or therapist that aparticular electrode has not been properly positioned, e.g., bycontrolling the BCI device or EEG device (or sending an instruction tothe BCI device or EEG device that causes the BCI device or EEG device)to light up the particular electrodes that have not been properly placedor to light up those electrodes in a different color from the remainingproperly positioned electrodes. For example, the electrodes that havebeen properly positioned can be indicated with a green light, and theelectrodes that have not been properly positioned can be indicated witha red light. In some embodiments, a display on a compute device (e.g.,the compute device 130 or a separate compute device associated with ahealthcare professional or therapist) can be configured to indicatewhich electrodes have not been properly positioned. The healthcareprofessional or therapist can then adjust the BCI device or the EEGdevice until the electrodes are properly positioned on the user's head.

At 904, a calibration can optionally be performed. For example, the usercan be assigned a pre-game calibration task, which can enable the BCIdevice or EEG device to be calibrated or set to the specific user'sbrain activities. In some embodiments, data collected during thecalibration can be used to uniquely adapt a model to be a user-specificmodel, as described above with reference to FIG. 3 . In an example, thecalibration can involve audio guidance (e.g., through a calm andrelaxing voice) to assist the user in entering a state of relaxation. Insome embodiments, the audio script can be based on induction techniquesemployed in hypnosis, such as, for example, the staircase method. Insome embodiments, the user can be given a looping or repeating animationthat the user can be asked to focus on. As the user proceeds through theinduction process, the user's EEG data and other data can be collectedand used to update the global classifier. The calibration phase cancontinue and be repeated before beginning a gameplay.

The gameplay or feedback-based meditation program can then initiate. At906, a scene including a first set of objects can be presented. The usercan be seated in a dark space, while one or more sensory elements (e.g.,visual, audio, haptic, tactile, gustatory) can be presented to the user.For example, a combination of sound and visual elements can bepresented. In some embodiments, the scene can be one of the examplescenes depicted in FIGS. 11A-14D. For illustrative purposes, 906-930 aredescribed with reference to a first example scene 1100, as depicted inFIGS. 11A-11E. FIGS. 11A-11E depict the scene 1100 as the userprogresses through the gameplay. In FIG. 11A, the scene can begin with afirst set of objects 1102, 1104. The user can be instructed to focus orrelax. With respect to the scene 1100, the user can be instructed tofocus on a black circle or dot 1102 (e.g., an object) in the center ofthe scene. While a black dot 1102 is provided with resect to the scene1100, it can be appreciated that in other embodiments, different objects(e.g., a visual element, an audio element, a haptic element, a gustatoryelement, or a tactile element) can be presented to the user for the userto focus on. In some embodiments, the user may be generally instructedto focus or relax, e.g., by focusing on the user's breathing or anotheractivity, by focusing on the user's body or self, etc. As such, in someembodiments, no object (e.g., no black dot 1102 or other object) may beprovided to a user to focus and/or relax.

While the scene is being presented and the user is focusing on the blackdot 1102, the BCI device or EEG device (and/or other sensors, e.g.,sensor(s) 120) can be configured to measure user data, including, forexample, EEG data, HRV data, etc., at 908. The user data can be sentfrom the BCI device or EEG device to a compute device (e.g., computedevice 130), and the compute device can process the user data. At 910,the compute device can determine whether the sensor data is sufficientfor evaluating a state of the user, e.g., as described above withrespect to FIGS. 6-8 .

If the data is not sufficient for evaluating the state of the user, thenthe process can return to 902. If the data is sufficient for evaluatingthe state of the user, then at 912, the compute device, e.g.,implementing a score generator (e.g., score generator 143), can use amodel to determine a score that is indicative of the state of the userbased on the user data. In particular, the compute device can input theuser data into the model, which can generate an output that is or isindicative of the score of the user. The model, as described withrespect to FIG. 3 above, can be a classifier such as, for example, aglobal score model 303 or a personalized score model 304. The score canbe based on or indicative of a measure of focus and relaxation of theuser, e.g., as determined based on the user's EEG data, HRV data, and/orother data.

While the scene 1100 is being presented, the compute device canrepeatedly or periodically at intervals (e.g., between about 1microsecond to about 10 seconds, including all subranges and valuestherebetween, including, for example, each 1 second), continue tocollect user data, at 908, and determine a score of the user, at 912,e.g., to monitor the state of the user. In particular, after determiningeach score, the compute device can determine whether the score is abovea threshold, at 914. When the user's score is above the threshold, thepresentation of the first set of objects can be modified, at 916. Forexample, the first set of objects 1104 can move toward forming apattern, such as, for example, a ring. FIG. 11B depicts the first set ofobjects 1104 forming a first ring. Alternatively or additionally, anadditional object (e.g., an audio element such as a sound) can bepresented, at 916. For example, as the objects move toward forming thering in FIG. 11B, a sound can play to indicate to the user that he isprogressing toward a first checkpoint (e.g., forming a first ring).

The process can then continue to 920, where the compute device candetermine whether the score has satisfied a predetermined metric. Insome embodiments, the score can satisfy the predetermined metric, e.g.,by being above the threshold for a certain period of time. In otherembodiments, the score can satisfy the predetermined metric by being acertain amount or percentage above the threshold. In some embodiments,other metrics can be used to evaluate when the score of the user, andtherefore the state of the user, has progressed sufficiently, e.g., toreach a first checkpoint. When the score has not satisfied thepredetermined metric, then the first set of objects 1104 can continue tobe presented, at 924. If the presentation of the objects has beenmodified, e.g., at 916, then the objects can be presented according totheir modified presentation. The process can then return to 908, e.g.,as additional user data is collected for generating another score.

When the score has satisfied the predetermined metric, then at 926, thecompute device can determine whether the feedback-based meditationprogram is complete. In some embodiments, the program can complete aftera predetermined period of time (e.g., between about 5 minutes and about20 minutes, including all subranges and values therebetween, including,for example, 10 minutes). In some embodiments, the program can completeafter the user has progressed through a plurality of checkpoints (e.g.,formed a sufficient number of rings in the example of FIGS. 11A-11E).When the program is complete, then it can stop, at 930. Alternatively,when the program is not complete, then the program can optionallyadvance onto a subsequent set of objects, at 928. For example, withrespect to the scene 1100, when the score has satisfied thepredetermined metric, then the user has reached a first checkpoint andthe first set of objects 1104 may have formed a first ring as depictedin FIG. 11B. A second set of objects 1106, as depicted in FIG. 11C, canthen form. The process can then repeat with the second set of objects1106 until a second checkpoint is reached and a second ring is formed,as depicted in FIG. 11D.

This process can continue, e.g., through a series of checkpoints wheresubsequent sets of objects are presented after preceding sets of objectsform a pattern, until a certain number of rings have been formed, asdepicted in FIG. 11E. In the example shown, this is six rings, but itcan be appreciated that any number of rings can be formed. When the lastcheckpoint has been reached (e.g., a last set of objects 1108 has formeda ring as shown in FIG. 11E), then the process can end, at 930. In someembodiments, the scene at the end of the gameplay can include anadditional object to the user. For example, the scene 1100 includes anadditional visual element “GREAT JOB!” 1110. In some embodiments, thescene at the end of the gameplay can also include an icon or other userinterface element that the healthcare professional or therapist canselect to return to a home interface. From that home interface, thehealthcare professional or therapist can then re-initiate thefeedback-based meditation program, e.g., with the same user or adifferent user.

Referring back to 914, when the user's score is below the threshold (orsustained below the threshold), the first set of objects can continue tobe presented, at 918, but in some embodiments, the presentation of thefirst set of objects can change. For example, the first set of objectsmay move toward more chaotic motion and not form any pattern.Alternatively, the first set of objects can scatter or lose its pattern.The process can continue looping or return back to 908, where additionaluser data is measured and then used to determine an updated score forthe user. In some embodiments, the first set of objects can fade away,e.g., when a user's score remains below a threshold for a predeterminedperiod of time.

While not depicted, it can be appreciated that the healthcareprofessional or therapist can halt or stop the method 900 at any stage.For example, if the healthcare professional determines that there is asafety risk or that the user is not progressing as intended, then thehealthcare professional can halt the gameplay. In some embodiments, thehealthcare professional can restart the gameplay with the user or thehealthcare professional can select a different feedback-based meditationprogram with a different scene to present to the user.

When the user has completed the gameplay (or multiple gameplays), theuser can be ready for receiving a drug treatment and/or a procedure. Inparticular, the gameplay (or multiple gameplays) can enable the user tobe in a focused or relaxed state, e.g., with a suitable set forreceiving the drug treatment or the procedure. For a drug treatment,e.g., involving a psychedelic, the process can continue to the method1000 depicted in FIG. 10 .

Referring now to FIGS. 12A-14D, three other examples of scenes that canbe implemented as part of a feedback-based meditation program (e.g., thefeedback-based meditation program described with reference to FIGS. 9Aand 9B) are depicted.

FIGS. 12A-12B depict an example scene that starts at 1200 and progressesto 1202. The scene can have objects such as particles. When the scene isinitially presented (e.g., at 906 of the method 900), the particles canbe randomly placed or scattered. The user can be instructed to focus orrelax, e.g., to focus on his breathing, his body, or an object. One ormore sensors (e.g., a BCI device, EEG device, PPG device, etc.) can beconfigured to collect user data after the user has been instructed tofocus or relax (e.g., at 908 of the method 900). A score indicative ofthe state of the user (e.g., a measure of focus and/or relaxation of theuser) can then be generated using a model that processes the user data(e.g., at 912 of the method 900). When the user's score is above acertain threshold, the presentation of the particles can be modified,e.g., the parties can move toward a specific pattern formation, asdepicted in FIG. 12B. Additionally or alternatively, an additionalobject can be presented, e.g., a sound that indicates to the user thathis state is optimal (or approaching optimal). When the user's score isbelow the threshold, then the particles can continue to be presented,e.g., in a randomly placed manner or chaotic pattern-less state. In someembodiments, once a pattern has been formed and maintained for a presetamount of time (e.g., once the user's score has satisfied apredetermined metric by, for example, remaining above a threshold for aset period of time), then the user's continuing performance above thethreshold can generate a new object (e.g., a new sound) and/or move theparticles toward a new pattern formation. This process can continue,e.g., through a series of checkpoints where additional patterns areformed (e.g., 906-926), until a certain number of patterns have beenformed. For example, the scene can continue until the user forms fourpatterns. The gameplay can then end (e.g., at 930 of the method 900).

FIGS. 13A-13E depict another example scene that starts at 1300 andprogresses through to 1302, 1304, 1306, and 1308 according toembodiments. The scene can start out in darkness, as depicted in FIG.13A. The user can be instructed to focus or relax, e.g., to focus on hisbreathing, his body, or an object. One or more sensors (e.g., a BCIdevice, EEG device, PPG device, etc.) can be configured to collect userdata after the user has been instructed to focus or relax (e.g., at 908of the method 900). A score indicative of the state of the user (e.g., ameasure of focus and/or relaxation) can then be generated using a modelthat processes the user data (e.g., at 912 of the method 900). When theuser's score is above a certain threshold, geometric shapes (e.g.,objects) can be generated starting near a center of the scene andexpanding outwards, as depicted in FIGS. 13B and 13C. In the embodimentdepicted, the geometric shapes can form mandala-like pattern, but it canbe appreciated that other patterns and/or designs can be formed. In someembodiments, once a pattern has been fully formed and maintained for apreset amount of time (e.g., once the user's score has satisfied apredetermined metric by, for example, remaining above a threshold for aset period of time), then the user's continuing performance above thethreshold can return to a dark scene and generate a new geometric shape,e.g., as depicted in FIG. 13D, and expand outward to form anew pattern,e.g., as depicted in FIG. 13E. When the user's score is below thethreshold, then shapes do not form. When the user's score remains belowthe threshold for a preset amount of time, geometric shapes that havealready formed and/or a geometric pattern that has formed can fade away,and the user must start again (e.g., with a new pattern forming, e.g.,as depicted in FIGS. 13D and 13E, once their score is above thethreshold again). In some embodiments, the scene can continue until acertain number of patterns have formed. Alternatively, the scene cancontinue until a predetermined period of time as elapsed (e.g., betweenabout 5 minutes and about 20 minutes, including, for example, 10minutes). In this latter instance, the scene may not have anycheckpoints to complete. The gameplay can then end (e.g., at 930 of themethod 900).

FIGS. 14A-14D depict yet another example of a scene, according toembodiments. FIGS. 14A-14D depict the scene as it starts at 1400 andthen progress through to 1402, 1404, and 1406. The scene starts out in adark forest setting, as depicted in FIG. 14A. The user can be instructedto focus or relax, e.g., to focus on his breathing, his body, or anobject. One or more sensors (e.g., a BCI device, EEG device, PPG device,etc.) can be configured to collect user data after the user has beeninstructed to focus or relax (e.g., at 908 of the method 900). A scoreindicative of the state of the user (e.g., a measure of focus and/orrelaxation) can then be generated using a model that processes the userdata (e.g., at 912 of the method 900). When the user's score is above acertain threshold, then flowers (e.g., objects) in the scene can change,e.g., grow and bloom, as shown in FIG. 14B. When the user's score isbelow the threshold, then the flowers can turn a different color (e.g.,purple), as shown in FIG. 14C. When the user's score remains below thethreshold for a preset amount of time, the flowers can break into petalsand gently float away, as depicted in FIG. 14D, and the user must startagain (e.g., with a new set of flowers, once their score is above thethreshold again). The scene can continue until a predetermined period oftime as elapsed (e.g., between about 5 minutes and about 20 minutes,including, for example, 10 minutes). In this latter instance, the scenemay not have any checkpoints to complete. The gameplay can then end(e.g., at 930 of the method 900).

In some embodiments, after a user has completed the feedback-basedmeditation or other multi-sensory exercise or gameplay, the user canhave a set or state that is suitable for receiving a drug treatment. Insome embodiments, a healthcare professional can then administer a drugtreatment to the user. The drug treatment can be, for example,Psilocybin, Ketamine, Esketamine, R-Ketamine, RL-007 (e.g., forschizophrenia), Ibogaine, Deuterated Etifoxine, N-Acetylcysteine,methylenedioxy-methylamphetamine (MDMA),N-methyl-1-(3,4-methylenedioxyphenyl)propan-2-amine),methylenedioxy-methylamfetamine, 3,4-methylenedioxymethamphetamine,3,4-Methylenedioxyamphetamine (MDA), Salvinorin A, DeuteratedMitragynine, Noribogaine, Dimethyltryptamine (DMT), N,N-DMT,D-Cycloserine, or other drug treatments with acute effects on a centralnervous system. In some embodiments, a drug treatment can beadministered to a user separate from a feedback-based meditation orother multi-sensory exercise, as described above.

In some embodiments, the healthcare professional can administer the drugtreatment based on the user's scores, e.g., during the feedback-basedmeditation. For example, if the user's score indicates that the user hasreached a higher state of relaxation and/or focus, then the healthcareprofessional may administer a high dose of a drug treatment. In someembodiments, depending on whether the user's scores are low or close toa threshold or significantly higher than a threshold (e.g., a certainpercentage or amount above the threshold), then the healthcareprofessional may administer more or less of a drug. For example, if auser's score indicates that the user is less likely to be in a suitablestate for receiving a drug treatment, then the healthcare professionalmay administer a lower dose the drug, e.g., to have a smaller effect onthe user. Alternatively, if the user's score indicates that the user ismore likely to be in a suitable state for receiving a drug treatment,then the healthcare professional may administer a higher dose of thedrug. In some embodiments, the healthcare professional may alsoadminister a smaller dose of a drug when a user is making reoccurringvisits to a treatment site. In other words, if a user is periodically orrepeatedly undergoing drug treatment, then smaller doses of the drugtreatment may be delivered over time vs. a single larger dose.

In some embodiments, a feedback-based adaptive settings program may beimplemented, e.g., after a user has received a drug treatment and isexperiencing acute condition associated with the drug treatment. FIG. 10illustrates a method 1000 of performing a feedback-based adaptivesettings process, according to an embodiment. The feedback-basedadaptive settings process can be used to inform a therapy system (e.g.,therapy system 100) of a user's state while that user is experiencingthe effects of a drug treatment. An example of such a feedback-basedadaptive settings process 1000, when used when a user is having apsychedelic experience, is described in more detail in the section“Example 1” below. In some embodiments, the method 400 can be performedby a therapy system that is structurally and/or functionally similar tothe therapy system 100, as shown and described with respect to FIG. 1 ,and/or include elements that are similar to those described with respectto FIGS. 2 and 3 .

In some embodiments, a user can wear a BCI device, an EEG device, and/ora PPG device, e.g., for measuring user data (e.g., EEG data, HRV data,etc.). The BCI device, EEG device, and/or PPG device can be an exampleof a sensor 120. At 1002, an impedance check can optionally beperformed, e.g., to confirm that the BCI device or EEG device has beenproperly placed on the user. This can be similar to 902 of method 900,as described with respect to FIG. 9A, and therefore further details ofthis step are not provided herein again. Optionally, calibration datacan also be collected, at 1004. This process can be similar to 904 ofmethod 900, as described with reference to FIG. 9A. For example,calibration data can be collected and labeled via time locking withspecific multi-sensory experiences (e.g., audio-video experiences). Forexample, a multi-sensory experience for use with calibration can be anaudio-video experience that includes a series of calming and relaxingaudio-visual environments (e.g., presented by headphones, displays,and/or projections), interspersed with periods of no environmentalstimulation. This can provide a rough estimate of when the user isrelaxed or not, and what the user's data (e.g., EEG data, HRV data,etc.) looked like when he was relaxed or not. The labeled data from thecalibration phase can then be used to adapt a global classifier (e.g.,global score model 303) to a personalized classifier (e.g., apersonalized score model 304).

At 1006, a scene including a set of sensory elements (e.g., audio,visual, haptic, tactile, gustatory elements) can be presented, e.g.,using a multi-sensory presentation device. The multi-sensorypresentation device can be an example of a therapy device (e.g., therapydevice 110). In some embodiments, the multi-sensory presentation devicecan include one or more projectors, tactile devices (e.g., a tactilevest), sound machines or other audio devices, scent generating devices,etc. In some embodiments, presenting the scene can involve projecting ascene of a tranquil environment (e.g., a beach, a forest, a room). Forexample, a scene of a tranquil forest, lit by moonlight, can beprojected. In some embodiments, the scene can be projected on a specificsurface or specific structures, e.g., one or more 3D structures (e.g.,foam boxes) that can provide a feeling of depth and immersion to thescene. In some embodiments, a sound machine or headphones can playrelaxing sounds, e.g., the sounds of crickets chirping, a stream ofwater calmly babbling, a sound of wind gently rustling the leaves of atree, wave sounds, etc. In some embodiments, the sounds can be set intoa binaural format that also provides various levels of depth andimmersion to feel like the sounds are coming from particular locationsin the space (e.g., the rustling of leaves on the trees nearby soundcloser, and the babbling of the stream, which appears a distance away,sounds further away). In some embodiments, a tactile device such as atactile vest can gently rumble across the user's back, e.g., massagingthe user into relaxation. In some embodiments, a scent delivery devicecan delivery smells, e.g., of pine, damp moss, fresh air, ocean air,etc. In some embodiments, a temperature device can generate coolness orheat and/or moving air (e.g., via a fan).

At 1008, user data can be measured using one or more sensor(s). This canbe similar to 908 of method 900, described above with respect to FIG.9A. The user data can be sent from the sensor(s) to a compute device(e.g., compute device 130), and the compute device can process the userdata. At 1010, the compute device can determine whether the sensor datais sufficient for evaluating a state of the user, e.g., as describedabove with respect to FIGS. 6-8 .

If the data is not sufficient for evaluating the state of the user, thenthe process can return to 902. If the data is sufficient for evaluatingthe state of the user, then at 912, the compute device, e.g.,implementing a score generator (e.g., score generator 143), can use amodel to determine a score that is indicative of the state of the userbased on the user data. In particular, the compute device can input theuser data into the model, which can generate an output that is or isindicative of the score of the user. The model, as described withrespect to FIG. 3 above, can be a classifier such as, for example, aglobal score model 303 or a personalized score model 304. The score canbe based on or indicative of a measure of a user's relaxation or otherstate. While the scene 1100 is being presented, the compute device canrepeatedly or periodically at intervals (e.g., between about 1microsecond to about 10 seconds, including all subranges and valuestherebetween, including, for example, each 1 second), continue tocollect user data, at 1008, and determine a score of the user, at 1012,e.g., to monitor the state of the user.

At 1014, the compute device can determine how to modify a scene based onthe user's score. In some embodiments, after determining each score, thecompute device can determine how the score compares to a threshold. Whenthe user's score is far below a threshold (e.g., below a predefinedrange or deviation from the threshold), then the compute device canchange the setting, e.g., abruptly and/or drastically. For example, thecompute device can change a substantial majority or all of the elementsin the scene (e.g., switching from a woods scene to a beach scene).Additionally, the compute device can change the scene immediately orclose to immediately, e.g., within about 1 and about 10 seconds,including all values and sub-ranges therebetween. When the user's scoreis above or below the threshold by a small amount (e.g., within acertain range or deviation of the threshold), then the compute devicecan wait a predetermined period of time to see if the score increases,decreases, or remains the same. When the score remains the same ordecreases, then the compute device can be configured to change thesetting at a slower or less drastic rate. The compute device can also beconfigured to change less elements in the scene (e.g., switching from adarker woods setting to a lighter woods setting). For example, thenight-time forest that was initially projected can transition intodaytime. The sound of the wind and the crickets chirping can subside,while the sound of the water can become clearer. The tactile vestsensation can decrease slightly. The scent delivery device can increasethe release of pine scents. When the user's score remains well above thethreshold (e.g., above a predefined range or deviation from thethreshold), then the compute device can maintain the setting to be thesame or substantially the same, e.g., with minor changes occurring as toensure that the current setting does not become monotonous orsuperficial.

Once a predetermined period of time has elapsed, at 1016, then theprogram can stop, at 1030. In some embodiments, the predetermined periodof time can be a predetermined period of time after receiving a drugtreatment. In some embodiments, the program can continue until the userstops experiences effects from a drug treatment (e.g., until after apsychedelic experience is over). While the experience continues, theprocess can loop or return to 1008 to iteratively measure the user data,generate scores, and adapt the setting to the user based on his score.

FIG. 5 is a schematic diagram illustrating a closed-loop 500 for adigital therapy (or other multi-sensory experience or exercise),according to an embodiment. The closed-loop 500 can be performed by thetherapy system 100 as shown and described with respect to FIG. 1 . Theclosed-loop 500 can involve collecting sensor data 501 (e.g., EEGsignals) from a user undergoing a digital therapy. In some instances,the digital therapy can induce a behavioral response in the patient. Atthat time, a brain-computer interface (BCI) device (e.g., the therapydevice 110 as shown and described with respect to FIG. 1 ) cansimultaneously capture neural activity in the brain.

The closed-loop 500 can involve measuring changes in the mental state(s)of the user during the digital therapy using a set of metrics 502. Forexample, in some instances, a score generator (similar to scoregenerator 143 shown and described with respect to FIG. 1 ) can include,but is not limited to, patient-adaptive methods that measure states suchas stress, anxiety, and attention in substantially real time (10milliseconds, 100 milliseconds, 1 second, 2 seconds, 10 seconds, or, 20seconds) extract features from the sensor data that are indicative ofattention, visuo-spatial attention, stress, anxiety, mental fatigue,and/or sedation.

The closed-loop 500 can involve operating the digital therapy 503 byusing the user's sensor data (e.g., brain-state information) insubstantially real-time (e.g., 10 milliseconds, 100 milliseconds, 1second, 2 seconds, 10 seconds, or 20 seconds) to adjust the digitaltherapy 503 such that the digital therapy 503 is more effective for theuser. The digital therapy 503 can involve, but is not limited to, forexample, a virtual reality (VR) exposure therapy, cognitive behavioraltherapy (CBT) scenario therapy, an attention-bias modification training(ABMT) game, adaptive settings, and/or meditation exercises.

The closed-loop 500 can further involve adapting 504 the digital therapy503. Similarly stated, the closed-loop 500 can adaptively change thedigital therapy (e.g., adaptively changing a difficulty level of thedigital therapy, adaptively progressing a visual within the samedifficulty level of the digital therapy, and/or or adaptively changingthe system of selection/generation of a setting) in response to apatient's progress. In some instances, the patient's progress can be ameasure of mental state response to a digital setting. In someimplementations, a machine learning model (e.g., similar to the scoregenerator 143 as shown and described with respect to FIG. 1 ) can beused to generate a score to estimate a progression (e.g., acompleteness) of the digital therapy 503. Thereafter, the closed-loop500 can adaptively change the digital therapy based on the estimatedscore.

Example 1—Feedback-Based Multi-Sensory Programs

In some instances, the multi-sensory experience or exercise such as adigital therapy can involve performing a game and/or a congestive taskby the user. For example, in some instances, the digital therapy caninvolve performing an Eriksen flanker task. Because the user of thetherapy systems described above with respect to FIGS. 1-5 often haslimited movement (e.g., sitting in one position) while conducting thedigital therapy, in some instances, the game and/or the congestive taskexperience can show, for example, a first-person perspective that isfrom a fixed position. Otherwise, negative senses such as nausea mayarise.

In some instances, the game and/or the congestive task can involvegenerating vibrations through meditation. For example, a therapy device(e.g., therapy device 110) or compute device (e.g., compute device 130)can include one or more output devices, including a vibration generatingdevice (e.g., transducer). The compute device can be configured todetermine, e.g., via a score generator (e.g., score generator 143), ascore indicative of a meditation state of the user. The compute deviceand/or therapy device can then generate vibrations based on this scoreof the user. As stronger vibrations are generated by the user reachinghigher meditative states within the game and/or the congestive task,particles (e.g., that are randomly scattered and/or distributed) thatoccupy various points in the virtual space in the game and/or thecongestive task, can come together and form, for example, athree-dimensional (3D) and/or symmetric geometric pattern. In addition,in some instances, the user can be also provided with a binaural soundon which the user can focus. The binaural frequencies can assistinducing particular patterns of brain activity through the mechanism ofneural entrainment.

In an example embodiment, a user can visit a clinic for pre-treatmentsession (e.g., before using a drug treatment). The user can be sat in acomfortable position (e.g., in a chair or on the floor) and in a quietsetting. In the clinic, a clinician (e.g., a nurse, a physician, aspecialist, and/or other medically-trained professional) can place abrain-computer interface (BCI) device (e.g., a 2-4 electrode BCI device)and a virtual reality (VR) device on the user's head. The BCI device caninclude sensors (e.g., sensors 120) implemented as electrodes. Theclinician can begin a digital therapy, using the VR device that isoperatively coupled to a compute device (e.g., similar to the computedevice 130 as shown and described with respect to FIG. 1 ). The user canbe handed a controller(s) that is operatively coupled or connected tothe VR device. Upon an indication(s) from the user and/or the clinician,the digital therapy can begin, for example, via an interactiveapplication (e.g., presenter 147) executed by the VR device and/or thecompute device.

During the digital therapy, a set of instructions can be displayed atthe interactive application. Next, during a calibration phase, apre-game calibration task can begin, which can enable a brain-computerinterface (BCI) process and the VR device to be calibrated or set to theuser's brain activities. In some instances, the calibration phase canalso be used to collect labeled user data used to train a user-specificmachine learning model(s) (e.g., use combined data from the calibrationphase with a global machine learning model(s) to develop theuser-specific machine learning model(s)). In the calibration phase, anaudio-guided induction phase can be presented to put the user into arelaxed state. The user is then introduced to a feedback metaphor thatuser can learn to control by modulating his/her level of attentionand/or other mental states. In one example, the metaphor can involveplaying a more chaotic visual(s) and a higher volume noise(s) inresponse to incorrect behavior, and a more orderly visual(s) and lowervolume noise in response to correct behavior. In another example, themetaphor can involve playing a high tone audio in response to a correctbehavior and a low tone audio in response to an incorrect behavior. Thecalibration phase can continue and be repeated before beginning agameplay. In some instances, the clinician can stand by for a signalfrom the interactive application indicating that the user is in anappropriate mental state for the gameplay (e.g., based on the userattaining a sustained score, e.g., as calculated based on scoregenerator 143, that meets a predetermined threshold). In response todetermining that the user has an appropriate mental state, thecalibration phase can be ended, and optionally the clinician can removethe VR device and/or the BCI device. In some instances, completion ofthe gameplay can indicate that the user is ready for a dosing for thedrug treatment.

In some instances, the interactive application can include a therapistmode, in which the therapist can take control of an exercise and modifythe exercise to guide the user in a predefined manner. In someinstances, the digital therapy system can receive feedback from the user(e.g., by providing a questionnaire after the session or by estimatingthe user's gameplay performance) to determine whether the user hascertain preferences, e.g., a gameplay with binaural sounds or a quietgameplay.

As the user focuses on the sound, the user score (e.g., generated by thescore generator 143 shown and described with respect to FIG. 1 ) can bemeasured in real-time and reflected back to the user through visual andauditory feedback. Auditory feedback can be reflected in a volume of thebinaural sound, where higher scores can be associated to reducing volumeand lower scores can be associated to increasing volume. Visually, thiscan be shown by a level of “harmony” in the particles in a scene, wherehigher scores are linked to particles gently vibrating and movingtowards pattern-based formations and lower scores are linked toparticles not vibrating and moving towards random and messy positioning.In some instances, the user score can be also used for visual andauditory feedforward. In other words, the user's mental state score canbe used to control the gameplay and the user can be incentivized to movethe game forward/progress via the visual and auditory controlmechanisms.

In some embodiments, a user's EEG and HRV bio-signals can be used toinform a therapy system (e.g., similar to the therapy system 100 asshown and described with respect to FIG. 1 ) of a user's mental state inregard to optimal set (e.g., a suitable state) for a psychedelicexperience that is both positive and promotes long-term well-beingscores. An optimal set, for this example, can be a state of relaxation,low anxiety, low emotional excitability, positive affect, mentalclarity, openness to the experience (e.g., surrendering), and focusedattention.

The therapy system can use the EEG and HRV to generate a score for theuser's state and accordingly determine whether the score meets a minimumpre-set threshold (a personalized threshold determined, for example, bya specialist). The therapy system can include a therapy device 110 thatcan include a 3D-projection mapping system, a tactile vest, a binauralaudio system, and/or a scent delivery system that can effectively beused to control visual, haptic, auditory and olfactory aspects of asetting, respectively. Each of the 3D-projection mapping system, thetactile vest, the binaural audio system, and/or the scent deliverysystem can have a library of settings/states. The settings/states can begenerated in real-time. The library of settings/states can bedetermined/developed based on, for example, a broad consensus of what isrelaxing, positive, and focus-inducing.

In one example, a user can wear a brain-computer interface (BCI) devicewith a built-in PPG sensor that measures heart-rate variability. Theuser can wear a tactile vest with mechanical vibrators sewn into thevest and powered by electrical motors. The user can wear comfortableheadphones capable of playing binaural audio. The user can be in a roomwith 4 integrated projectors, each pointed at 1 of the 4 walls in theroom. The room can also include various foam boxes placed strategicallyaround the room that the projectors can project onto in addition to thewalls of the room. In addition, the room can also include a scentdelivery device positioned inconspicuously near the center of the room.

Next, an impedance check can be performed to ensure that the BCI signaloutput is sufficiently accurate. The real-time impedance check canappear as a diagram projected on the wall and a BCI device with 4glowing light emitting diodes (LEDs), each representing an electrode. Ifthe node glows green, then the electrode is making appropriate contactwith the skin. If the node glows red, then the user needs to readjustthe device. Once all 4 LEDs are green, the next phase begins.

Next, upon dosing with a drug treatment and after passing the impedancecheck, calibration data can be collected and labeled via time lockingwith specific audio-visual experiences. Audio-visual experiences usedfor labeling data can include a series of calming and relaxingaudio-visual environments presented by the projectors and headphones,interspersed with periods of no environmental stimulation. This canprovide a rough estimate of when an individual was relaxed or not andwhat the individual's EEG/HRV signals looked like when he/she wasrelaxed or not relaxed. The labeled data from the calibration phase canthen be used in conjunction with an existing machine learning model(e.g., a global in-treatment classifier) to develop a subject-specificmachine learning model (e.g., a subject-specific in-treatmentclassifier) that is more accurate to the user's unique brain signals.

Next, 3D projectors can be used to project a scenery of a tranquilforest, lit by the moonlight. The scenery can include trees. Some of thetrees in the scene can be projected onto the foam boxes, giving afeeling of depth and immersion to the scene that otherwise would feelflat. Headphones can play the sounds of crickets chirping, a stream ofwater calmly babbling, and the wind gently rustling the leaves of thetrees. The sounds can be set into a binaural format that also providesvarious levels of depth and immersion to feel like the sounds are comingfrom particular locations in the space (e.g. the rustling of leaves onthe trees nearby sound closer, and the babbling of the stream, whichappears in the distance sounds further away). The tactile vest cangently rumble across the participant's back, almost massaging the personinto relaxation. In addition, a scent delivery device can deliver smellsof pine, damp moss, and/or fresh air.

Next, sensor data can be collected from the user as described above anda score generator (similar to the score generator 143 as shown anddescribed with respect to FIG. 1 ) can generate an in-treatment scorefor the user's state. Based on the user's current in-treatment score, ifa user's score is far below a threshold, then the setting can changedrastically. For example, a scene of a beach on a warm, sunny dayappears through the projectors. The headphones can play the sound ofgently crashing waves. The tactile vest can decrease stimulation orchanges the location of vibration. The scent delivery device can deliverthe smells of salt water and coastal air.

If a user's score is above or below the threshold by a bit, the systemcan wait a pre-set amount of time and if the score has not improved oris wavering, then the setting can change but less drastically. Forexample, the night-time forest that was initially projected cantransition into daytime. The sound of the wind and the crickets chirpingcan subside, while the sound of the water can become clearer. Thetactile vest sensation can decrease slightly. The scent delivery devicecan increase the release of pine scents. If a user's score remains wellabove the threshold then the setting can remain mostly the same withminor changes occurring as to ensure that the current setting does notbecome monotonous or superficial. The therapy exercise can continue torepeat with a large library of potential settings until the psychedelicexperience is over. During the above process, data can be collected onthe user's setting preference based on whether the in-treatment scoregoes up or down in response to a specific setting. The data can then beused to refine a list of appropriate settings that can be used for aparticular individual.

In one example, a user can visit a clinic for a psychedelic treatment.The clinic can be fitted with projectors assigned to each wall, foamboxes to enable 3D-projection mapping (i.e., projecting onto multiplesurfaces to give a feeling of depth and immersion), and a scent deliverydevice near the center of the room. The user can sit in a comfortableposition on a couch. The user can put on a tactile vest. In the clinic anurse (or other medically-trained professional) can place a BCI/PPGdevice on the patient's head along with a pair of headphones. In someinstances, the nurse can initiate a real-time impedance check protocolvia a computer (e.g., similar to the compute device 130 as shown anddescribed with respect to FIG. 1 ) in the room. In some instances, thenurse can initiate an impedance check prior to starting a digitaltherapy session. The impedance check can run automatically throughoutthe digital therapy session to ensure the signal remains stable duringthe digital therapy session. Upon completion of the real-time impedancecheck, the computer can notify the nurse to begin dosing. A calibrationexperience can begin, which can enable a setting of a therapy device tobe optimized to the user's brain activity. Upon completion of thecalibration experience, a psychedelic treatment can begin. Uponcompletion of the psychedelic treatment, the nurse can remove devicesfrom the user.

Example 2—Feedback-Based Meditation Study

In an example study involving healthy individuals, a feedback-basedmeditation exercise or program was implemented, e.g., according tomethods described herein, such as, for example, method 900 describedwith reference to FIGS. 9A and 9B. The study involved the following:

-   -   A pre-session breath counting test and questionnaire    -   System setup    -   VR meditation exercise (e.g., a VR-implemented game)    -   A post-session breath counting test and questionnaire

During the pre-session breath counting test and questionnaire, each userwas asked to indicate their level of relaxation and calmness, and thenasked to perform a breath counting exercise (e.g., count a certainnumber of breaths). Specifically during the study, each user was askedto count 9 breaths.

During the system setup, a BCI device including one or more EEG sensors(e.g., sensor(s) 120) is connected to a meditation application (e.g.,implemented by a therapy device 110 and/or compute device 130). In thestudy, a Muse brain sensing headset was used. Each user was instructedto wear the BCI device according to specific protocols, e.g., ensuringheadset is securely in place but is comfortable, conducting an impedancecheck, etc. A VR device can then be connected to the meditationapplication (e.g., implemented by a therapy device and/or compute device130). The VR device can then be set up according to standard procedures,e.g., setting of room boundaries, setting of sit and stand positions,etc.). Each user was instructed to then wear the VR device. Inparticular, each user was instructed to wear the VR device over the BCIdevice.

During the VR meditation exercise, a scene the same as or substantiallythe same as the scene 1100 depicted in FIGS. 11A-11E was presented toeach user. In particular, each user was asked to form the six ringsdepicted in FIG. 11E. During the VR meditation exercise, a therapist(e.g., via a therapist application running on the compute device) hadcontrol to stop the VR meditation exercise at any time.

After each user completed the VR meditation game, the user performed apost-session breath counting test and questionnaire, similar to thepre-session breath counting test and questionnaire. In particular, theuser was again asked to provide their feedback on relaxation andcalmness. The user was also asked to provide feedback on the session(e.g., how did you find the overall experiences, did you have anyproblems with the experience, etc.). The user then performed a breathcounting test, e.g., counting breaths to 9.

Data was successfully captured from users in 35 sessions in the study.From the data collected during these sessions from these users, thefollowing was observed:

-   -   (74.3%) 26 out of 35 sessions showed improvement in calmness.        17.1% showed no change as those users were already “Very Calm”        prior to their VR session. 5.7% (2 sessions) showed decrease in        calmness after the VR meditation session    -   (91.4%) 32 out of 35 sessions showed improvement in relaxation.        2.8% showed no change. 5.7% (2 sessions) showed decrease in        relaxation after the VR meditation session    -   In 25 out of total 35 sessions (71% of sessions), users were        either Very Much Calm or Extremely Calm after the VR meditation        session    -   In 21 out of total 35 sessions (60% of sessions), users were        either Very Much Relaxed or Extremely Relaxed after the VR        meditation session

As evidenced by the observations above, the VR meditation exercise wasuseful in increasing calmness and relaxation in users.

Systems, devices, and methods described herein can be implemented as oneor more of the example embodiments below:

Embodiment 1: An apparatus, comprising: a virtual reality (VR) deviceconfigured to present at least one of a visual, olfactory, gustatory,auditory, or haptic signal to a user; a set of sensors configured tomeasure user data, the set of sensors including at least anelectroencephalography (EEG) sensor and a photoplethysmography (PPG)sensor; a memory; and a processor operatively coupled to the virtualreality device, the set of sensors, and the memory, the processorconfigured to: present, using the VR device, a scene including a firstset of objects to the user, the first set of objects including visual,olfactory, gustatory, auditory, or haptic elements; instruct a user toengage in an activity for increasing focus or relaxation; after the userhas been instructed to engage in the activity, iteratively perform untila score indicative of a state of the user satisfies a metric: measuring,using the set of sensors, the user data including at least EEG data andheart rate variability (HRV) data; determining, using a model trained tomeasure the state of the user, and based on the user data, the score ofthe user; and in response to the score of the user being above athreshold, modifying, using the VR device, the presentation of the firstset of objects such that the first set of objects form or follow apattern or presenting, using the VR device, an additional object; and inresponse to the score of the user satisfying the metric, present, usingthe VR device, a second set of objects to the user.

Embodiment 2: The apparatus of Embodiment 1, wherein the additionalobject is an auditory element including a tone, a sound effect, ormusic.

Embodiment 3: The apparatus of any one of Embodiments 1-2, wherein theprocessor is configured to instruct the user to engage in the activityfor increasing focus or relaxation by: instructing the user to focus onbreathing; instructing the user to focus on a visual element or anauditory element; instructing the user to focus on a body of the user;or performing a combination thereof.

Embodiment 4: The apparatus of any one of Embodiments 1-3, wherein theuser data further includes at least one of: heart rate data, respiratorydata, PPG data, galvanic skin response data, blood glucose data,pupillometry data, eye movement data, electromyography (EMG) data,electrodermal activity (EDA) data, blood pressure data, or bodytemperature.

Embodiment 5: The apparatus of any one of Embodiment 1-4, wherein thescore of the user satisfies the metric when the score of the userremains above the threshold for a set period of time.

Embodiment 6: The apparatus of any one of Embodiments 1-5, wherein thefirst set of objects includes a first set of visual elements, theprocessor configured to modify the presentation of the first set ofobjects by moving one or more visual elements of the first set of visualelements to form a pattern with the first set of visual elements.

Embodiment 7: The apparatus of any one of Embodiments 1-6, wherein thefirst set of objects includes a first set of audio elements, theprocessor configured to modify the presentation of the first set ofaudio elements by reducing an intensity or frequency of one or moreaudio element of the first set of audio elements.

Embodiment 8: The apparatus of any one of Embodiments 1-7, wherein theprocessor is configured to present the second set of objects while thefirst set of objects continue being presented.

Embodiment 9: The apparatus of any one of Embodiments 1-7, wherein theprocessor is further configured to fade away the first set of objectsbefore presenting the second set of objects.

Embodiment 10: The apparatus of any one of Embodiments 1-9, wherein theprocessor is configured to present, using the VR device, a plurality ofsets of objects to the user, the plurality of sets of objects includingthe first and second sets of objects, the processor configured topresent each subsequent set of objects of the plurality of sets ofobjects in response to the state of the user satisfying the metric afterthe set of objects immediately prior to the subsequent set of objects ispresented.

Embodiment 11: The apparatus of Embodiment 10, wherein the state of theuser satisfies the metric when the score of the user remains above thethreshold for a set period of time.

Embodiment 12: The apparatus of Embodiment 11, wherein the processor isfurther configured to adjust, based on the user data collected during aninitial period of time, the threshold to change a difficulty ofsatisfying the metric.

Embodiment 13: The apparatus of Embodiment 12, wherein the processor isconfigured to adjust the threshold by: determining, based on the userdata collected during the initial period of time, a competency group forthe user, the competency group being from a set of competency groups;and setting the threshold based on the competency group determined forthe user.

Embodiment 14: The apparatus of Embodiment 12, wherein the processorconfigured to adjust the threshold by: determining, based on the userdata collected during the initial period of time, a rate of progressionthrough the plurality of sets of objects; setting the threshold based onthe rate of progression.

Embodiment 15: The apparatus of Embodiment 14, wherein the processor isconfigured to set the threshold based on the rate of progression by:increasing the threshold when the rate of progression is greater thanpredetermined rate; and decreasing the threshold when the rate ofprogression is less than the predetermined rate.

Embodiment 16: The apparatus of Embodiment 10, wherein the processor isfurther configured to, in response to the state of the user satisfyingthe metric after the last set of objects of the plurality of sets ofobjects is presented, send an instructions to administer a drugtreatment to the user.

Embodiment 17: The apparatus of any one of Embodiments 1-16, wherein themodel is a user-specific model, the processor being further configuredto, prior to presenting the scene including the first set of objects:present, during a calibration phase, a set of calming environmentstimulations to the user interspersed with periods of no environmentalstimulation; measure user data during the calibration phase; and adapt,using the user data measured during the calibration phase, a genericmodel trained to measure a state of a user to be the user-specificmodel.

Embodiment 18: The apparatus of any one of Embodiments 1-17, wherein theprocessor is further configured to, prior to presenting the scene:determine whether an impedance measured by the EEG sensor falls within apredetermined range of impedances; in response to the impedance beingwithin the predetermined range, present an indication that the EEGsensor is properly positioned; in response to the impedance not beingwithin the predetermined range, present an indication that the EEGsensor needs to be readjusted.

Embodiment 19: The apparatus of any one of Embodiments 1-18, wherein inresponse to the score of the user being below the threshold, modifying,using the VR device, the presentation of the first set of objects suchthat the first set of objects regress to an earlier state.

Embodiment 20: An apparatus, comprising: a virtual reality (VR) deviceconfigured to present at least one of a visual, olfactory, gustatory,auditory, or haptic signal to a user; one or more electroencephalography(EEG) sensors configured to measure EEG data of the user; a memory; anda processor operatively coupled to the virtual reality device, the oneor more EEG sensors, and the memory, the processor configured to:present, using the VR device, a scene including a first set of objectsto the user, the first set of objects including visual, olfactory,gustatory, auditory, or haptic elements; instruct a user to engage in anactivity for increasing focus or relaxation; after the user has beeninstructed to engage in the activity, iteratively perform until a scoreindicative of a state of the user satisfies a metric: measuring, usingthe one or more EEG sensors, the EEG data; determining, using a modeltrained to measure the state of the user, and based on the EEG data, thescore of the user; and in response to the score of the user being abovea threshold, modifying, using the VR device, the presentation of thefirst set of objects such that the first set of objects form or follow apattern or presenting, using the VR device, an additional object; and inresponse to the score of the user satisfying the metric, present, usingthe VR device, a second set of objects to the user.

Embodiment 21: The apparatus of Embodiment 20, wherein the additionalobject is an auditory element including a tone, a sound effect, ormusic.

Embodiment 22: The apparatus of any one of Embodiments 20-21, whereinthe processor is configured to instruct the user to engage in theactivity for increasing focus or relaxation by: instructing the user tofocus on breathing; instructing the user to focus on a visual element oran auditory element; instructing the user to focus on a body of theuser; or performing a combination thereof.

Embodiment 23: The apparatus of any one of Embodiments 20-22, whereinthe score of the user satisfies the metric when the score of the userremains above the threshold for a set period of time.

Embodiment 24: The apparatus of any one of Embodiments 20-23, whereinthe first set of objects includes a first set of visual elements, theprocessor configured to modify the presentation of the first set ofobjects by moving one or more visual elements of the first set of visualelements to form a pattern with the first set of visual elements.

Embodiment 25: The apparatus of any one of Embodiments 20-24, whereinthe first set of objects includes a first set of audio elements, theprocessor configured to modify the presentation of the first set ofaudio elements by reducing an intensity or frequency of one or moreaudio element of the first set of audio elements.

Embodiment 26: The apparatus of any one of Embodiments 20-25, whereinthe processor is configured to present, using the VR device, a pluralityof sets of objects to the user, the plurality of sets of objectsincluding the first and second sets of objects, the processor configuredto present each subsequent set of objects of the plurality of sets ofobjects in response to the state of the user satisfying the metric afterthe set of objects immediately prior to the subsequent set of objects ispresented.

Embodiment 27: The apparatus of Embodiment 26, wherein the state of theuser satisfies the metric when the score of the user remains above thethreshold for a set period of time.

Embodiment 28: The apparatus of Embodiment 27, wherein the processor isfurther configured to adjust, based on the user data collected during aninitial period of time, the threshold to change a difficulty ofsatisfying the metric.

Embodiment 29: The apparatus of Embodiment 27, wherein the processor isfurther configured to, in response to the state of the user satisfyingthe metric after the last set of objects of the plurality of sets ofobjects is presented, send an instructions to administer a drugtreatment to the user.

Embodiment 30: The apparatus of any one of Embodiments 20-29, whereinthe processor is further configured to, prior to presenting the scene:determine whether an impedance measured by each of the one or more EEGsensors falls within a predetermined range of impedances; in response tothe impedance measured by an EEG sensor of the one or more EEG sensorsbeing within the predetermined range, present an indication that thatEEG sensor is properly positioned; in response to the impedance measuredby an EEG sensor of the one or more EEG sensors not being within thepredetermined range, present an indication that that EEG sensor needs tobe readjusted.

Embodiment 31: A method, comprising: presenting, using a virtual reality(VR) device, a scene including a first set of objects to the user, thefirst set of objects including visual, olfactory, gustatory, auditory,or haptic elements; instructing a user to engage in an activity forincreasing focus or relaxation; after the user has been instructed toengage in the activity, iteratively performing until a score indicativeof a state of the user satisfies a metric: measuring, using a set ofsensors including an electroencephalography (EEG) sensor and aphotoplethysmography (PPG) sensor, the user data including at least EEGdata and heart rate variability (HRV) data; determining, using a modeltrained to measure the state of the user, and based on the user data,the score of the user; and in response to the score of the user beingabove a threshold, modifying, using the VR device, the presentation ofthe first set of objects such that the first set of objects form orfollow a pattern or presenting, using the VR device, an additionalobject; and in response to the score of the user satisfying the metric,presenting, using the VR device, a second set of objects to the user.

Embodiment 32: An apparatus, comprising: a multi-sensory presentationdevice configured to present at least one of a visual, olfactory,gustatory, auditory, or haptic signal to a user; a set of sensorsconfigured to measure user data, the set of sensors including at leastan electroencephalography (EEG) sensor and a photoplethysmography (PPG)sensor; a memory; a processor operatively coupled to the memory, themulti-sensory presentation device, and the set of sensors, the processorconfigured to: present, using the multi-sensory presentation device andafter the user has received a drug treatment, a scene to the user, thescene including a first set of visual, olfactory, gustatory, auditory,or haptic elements; while the scene is being presented, iterativelyperform: measuring, using the set of sensors, user data including atleast EEG data and heart rate variability (HRV) data; determining, usinga model trained to measure a state of the user, and based on the userdata, a score of the user indicative of a state of the user; in responseto the score being lower than a threshold, modifying, based on the scoreof the user, the scene to include a second set of visual, olfactory,gustatory, auditory, or haptic elements different from the first set ofvisual, olfactory, gustatory, auditory, or haptic elements; and inresponse to the score being greater than the threshold for a set periodof time, modifying the scene to include a third set of visual,olfactory, gustatory, auditory, or haptic elements different from thefirst and second sets of visual, olfactory, gustatory, auditory, orhaptic elements; and continue to present the scene to the user until apredetermined period of time has elapsed from the user receiving thedrug treatment.

Embodiment 33: The apparatus of Embodiment 32, wherein the multi-sensorydevice is configured to present at least two of visual, olfactory,gustatory, auditory, or haptic signals.

Embodiment 34: The apparatus of any one of Embodiments 32-33, whereinthe multi-sensory device includes a virtual reality (VR) device, aprojector, smart glasses, or a parallax screen configured to generate avisual environment.

Embodiment 35: The apparatus of Embodiment 34, wherein modifying thescene to include the second set of visual, olfactory, gustatory,auditory, or haptic elements includes changing from a first visualenvironment to a second visual environment, and modifying the scene toinclude the third set of visual, olfactory, gustatory, auditory, orhaptic elements includes changing a subset of the first set of visual,olfactory, gustatory, auditory, or haptic elements in the first visualenvironment.

Embodiment 36: The apparatus of any one of Embodiments 32-35, whereinthe multi-sensory device includes a haptic vest configured to generate avibration.

Embodiment 37: The apparatus of Embodiment 36, wherein modifying thescene to include the second or third sets of visual, olfactory,gustatory, auditory, or haptic elements includes changing the vibrationgenerated by the haptic vest or moving a location of the vibrationgenerated by the haptic vest.

Embodiment 38: The apparatus of any one of Embodiments 32-37, whereinthe user data further includes at least one of: heart rate data,respiratory data, PPG data, galvanic skin response data, blood glucosedata, pupillometry data, eye movement data, electromyography (EMG) data,electrodermal activity (EDA) data, blood pressure data, or bodytemperature.

Embodiment 39: The apparatus of any one of Embodiments 32-37, whereinthe processor is configured to modify the scene to include the secondset of visual, olfactory, gustatory, auditory, or haptic elements at afirst rate, and the processor is configured to modify the scene toinclude the third set of visual, olfactory, gustatory, auditory, orhaptic elements at a second rate, the second rate being slower than thefirst rate.

Embodiment 40: The apparatus of any one of Embodiments 32-37, whereinthe processor is configured to modify the scene to include the secondset of visual, olfactory, gustatory, auditory, or haptic elements byabruptly removing the first set of visual, olfactory, gustatory,auditory, or haptic elements and presenting the second set of visual,olfactory, gustatory, auditory, or haptic elements, and the processor isconfigured to modify the scene to include the third set of visual,olfactory, gustatory, auditory, or haptic elements by gradually fadingaway the first set of visual, olfactory, gustatory, auditory, or hapticelements and presenting the third set of visual, olfactory, gustatory,auditory, or haptic elements.

Embodiment 41: The apparatus of any one of Embodiments 32-40, wherein adegree of difference between the second set of visual, olfactory,gustatory, auditory, or haptic elements and the first set of visual,olfactory, gustatory, auditory, or haptic elements is greater than thatbetween the third set of visual, olfactory, gustatory, auditory, orhaptic elements and the first set of visual, olfactory, gustatory,auditory, or haptic elements.

Embodiment 42: The apparatus of any one of Embodiments 32-41, whereinthe processor is further configured to, prior to presenting the scene:determine whether an impedance measured by the EEG sensor falls within apredetermined range of impedances; in response to the impedance beingwithin the predetermined range, present an indication that the EEGsensor is properly positioned; in response to the impedance not beingwithin the predetermined range, present an indication that the EEGsensor needs to be readjusted.

Embodiment 43: An apparatus, comprising: a multi-sensory presentationdevice configured to present at least one of a visual, olfactory,gustatory, auditory, or haptic signal to a user; one or moreelectroencephalography (EEG) sensors configured to measure EEG data ofthe user; a memory; a processor operatively coupled to the memory, themulti-sensory presentation device, and the one or more EEG sensors, theprocessor configured to: present, using the multi-sensory presentationdevice and after the user has received a drug treatment, a scene to theuser, the scene including a first set of visual, olfactory, gustatory,auditory, or haptic elements; while the scene is being presented,iteratively perform: measuring, using the one or more EEG sensors, theEEG data; determining, using a model trained to measure a state of theuser, and based on the EEG data, a score of the user indicative of astate of the user; in response to the score being lower than athreshold, modifying, based on the score of the user, the scene toinclude a second set of visual, olfactory, gustatory, auditory, orhaptic elements different from the first set of visual, olfactory,gustatory, auditory, or haptic elements; and in response to the scorebeing greater than the threshold for a set period of time, modifying thescene to include a third set of visual, olfactory, gustatory, auditory,or haptic elements different from the first and second sets of visual,olfactory, gustatory, auditory, or haptic elements; and continue topresent the scene to the user until a predetermined period of time haselapsed from the user receiving the drug treatment.

Embodiment 44: The apparatus of Embodiment 43, wherein the multi-sensorydevice is configured to present at least two of visual, olfactory,gustatory, auditory, or haptic signals.

Embodiment 45: The apparatus of any one of Embodiments 43-44, whereinthe multi-sensory device includes a virtual reality (VR) device, aprojector, smart glasses, or a parallax screen configured to generate avisual environment.

Embodiment 46: The apparatus of Embodiment 45, wherein modifying thescene to include the second set of visual, olfactory, gustatory,auditory, or haptic elements includes changing from a first visualenvironment to a second visual environment, and modifying the scene toinclude the third set of visual, olfactory, gustatory, auditory, orhaptic elements includes changing a subset of the first set of visual,olfactory, gustatory, auditory, or haptic elements in the first visualenvironment.

Embodiment 47: The apparatus of any one of Embodiments 43-46, whereinthe multi-sensory device includes a haptic vest configured to generate avibration.

Embodiment 48: The apparatus of Embodiment 47, wherein modifying thescene to include the second or third sets of visual, olfactory,gustatory, auditory, or haptic elements includes changing the vibrationgenerated by the haptic vest or moving a location of the vibrationgenerated by the haptic vest.

Embodiment 49: The apparatus of any one of Embodiments 43-48, whereinthe processor is configured to modify the scene to include the secondset of visual, olfactory, gustatory, auditory, or haptic elements at afirst rate, and the processor is configured to modify the scene toinclude the third set of visual, olfactory, gustatory, auditory, orhaptic elements at a second rate, the second rate being slower than thefirst rate.

Embodiment 50: The apparatus of any one of Embodiments 43-48, whereinthe processor is configured to modify the scene to include the secondset of visual, olfactory, gustatory, auditory, or haptic elements byabruptly removing the first set of visual, olfactory, gustatory,auditory, or haptic elements and presenting the second set of visual,olfactory, gustatory, auditory, or haptic elements, and the processor isconfigured to modify the scene to include the third set of visual,olfactory, gustatory, auditory, or haptic elements by gradually fadingaway the first set of visual, olfactory, gustatory, auditory, or hapticelements and presenting the third set of visual, olfactory, gustatory,auditory, or haptic elements.

Embodiment 51: The apparatus of any one of Embodiments 43-48, wherein adegree of difference between the second set of visual, olfactory,gustatory, auditory, or haptic elements and the first set of visual,olfactory, gustatory, auditory, or haptic elements is greater than thatbetween the third set of visual, olfactory, gustatory, auditory, orhaptic elements and the first set of visual, olfactory, gustatory,auditory, or haptic elements.

Embodiment 52: A method, comprising: presenting, using a multi-sensorypresentation device and after the user has received a drug treatment, ascene to the user, the scene including a first set of visual, olfactory,gustatory, auditory, or haptic elements; while the scene is beingpresented, iteratively performing: measuring, using a set of sensorsincluding an electroencephalography (EEG) sensor and aphotoplethysmography (PPG) sensor, user data including at least EEG dataand heart rate variability (HRV) data; determining, using a modeltrained to measure a state of the user, and based on the user data, ascore of the user indicative of a state of the user; in response to thescore being lower than a threshold, modifying, based on the score of theuser, the scene to include a second set of visual, olfactory, gustatory,auditory, or haptic elements different from the first set of visual,olfactory, gustatory, auditory, or haptic elements; and in response tothe score being greater than the threshold for a set period of time,modifying the scene to include a third set of visual, olfactory,gustatory, auditory, or haptic elements different from the first andsecond sets of visual, olfactory, gustatory, auditory, or hapticelements; and continuing to present the scene to the user until apredetermined period of time has elapsed from the user receiving thedrug treatment.

It should be understood that the disclosed embodiments are notrepresentative of all claimed innovations. As such, certain aspects ofthe disclosure have not been discussed herein. That alternateembodiments may not have been presented for a specific portion of theinnovations or that further undescribed alternate embodiments may beavailable for a portion is not to be considered a disclaimer of thosealternate embodiments. Thus, it is to be understood that otherembodiments can be utilized, and functional, logical, operational,organizational, structural and/or topological modifications may be madewithout departing from the scope of the disclosure. As such, allexamples and/or embodiments are deemed to be non-limiting throughoutthis disclosure

Some embodiments described herein relate to methods. It should beunderstood that such methods can be computer implemented methods (e.g.,instructions stored in memory and executed on processors). Where methodsdescribed above indicate certain events occurring in certain order, theordering of certain events can be modified. Additionally, certain of theevents can be performed repeatedly, concurrently in a parallel processwhen possible, as well as performed sequentially as described above.Furthermore, certain embodiments can omit one or more described events.

Some embodiments described herein relate to a computer storage productwith a non-transitory computer-readable medium (also can be referred toas a non-transitory processor-readable medium) having instructions orcomputer code thereon for performing various computer-implementedoperations. The computer-readable medium (or processor-readable medium)is non-transitory in the sense that it does not include transitorypropagating signals per se (e.g., a propagating electromagnetic wavecarrying information on a transmission medium such as space or a cable).The media and computer code (also can be referred to as code) may bethose designed and constructed for the specific purpose or purposes.Examples of non-transitory computer-readable media include, but are notlimited to, magnetic storage media such as hard disks, floppy disks, andmagnetic tape; optical storage media such as Compact Disc/Digital VideoDiscs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), andholographic devices; magneto-optical storage media such as opticaldisks; carrier wave signal processing modules; and hardware devices thatare specially configured to store and execute program code, such asApplication-Specific Integrated Circuits (ASICs), Programmable LogicDevices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM)devices. Other embodiments described herein relate to a computer programproduct, which can include, for example, the instructions and/orcomputer code discussed herein.

Some embodiments and/or methods described herein can be performed bysoftware (executed on hardware), hardware, or a combination thereof.Hardware modules may include, for example, a general-purpose processor,a field programmable gate array (FPGA), and/or an application specificintegrated circuit (ASIC). Software modules (executed on hardware) canbe expressed in a variety of software languages (e.g., computer code),including C, C++, Java™, Ruby, Visual Basic™, and/or otherobject-oriented, procedural, or other programming language anddevelopment tools. Examples of computer code include, but are notlimited to, micro-code or micro-instructions, machine instructions, suchas produced by a compiler, code used to produce a web service, and filescontaining higher-level instructions that are executed by a computerusing an interpreter. For example, embodiments can be implemented usingPython, Java, JavaScript, C++, and/or other programming languages andsoftware development tools. For example, embodiments may be implementedusing imperative programming languages (e.g., C, Fortran, etc.),functional programming languages (Haskell, Erlang, etc.), logicalprogramming languages (e.g., Prolog), object-oriented programminglanguages (e.g., Java, C++, etc.) or other suitable programminglanguages and/or development tools. Additional examples of computer codeinclude, but are not limited to, control signals, encrypted code, andcompressed code.

In order to address various issues and advance the art, the entirety ofthis application (including the Cover Page, Title, Headings, Background,Summary, Brief Description of the Drawings, Detailed Description,Claims, Abstract, Figures, Appendices, and otherwise) shows, by way ofillustration, various embodiments in which the claimed innovations canbe practiced. The advantages and features of the application are of arepresentative sample of embodiments only and are not exhaustive and/orexclusive. They are presented to assist in understanding and teach theclaimed principles.

The drawings primarily are for illustrative purposes and are notintended to limit the scope of the subject matter described herein. Thedrawings are not necessarily to scale; in some instances, variousaspects of the subject matter disclosed herein can be shown exaggeratedor enlarged in the drawings to facilitate an understanding of differentfeatures.

The acts performed as part of a disclosed method(s) can be ordered inany suitable way. Accordingly, embodiments can be constructed in whichprocesses or steps are executed in an order different than illustrated,which can include performing some steps or processes simultaneously,even though shown as sequential acts in illustrative embodiments. Putdifferently, it is to be understood that such features may notnecessarily be limited to a particular order of execution, but rather,any number of threads, processes, services, servers, and/or the likethat may execute serially, asynchronously, concurrently, in parallel,simultaneously, synchronously, and/or the like in a manner consistentwith the disclosure. As such, some of these features may be mutuallycontradictory, in that they cannot be simultaneously present in a singleembodiment. Similarly, some features are applicable to one aspect of theinnovations, and inapplicable to others.

The phrase “and/or,” as used herein in the specification and in theembodiments, should be understood to mean “either or both” of theelements so conjoined, i.e., elements that are conjunctively present insome cases and disjunctively present in other cases. Multiple elementslisted with “and/or” should be construed in the same fashion, i.e., “oneor more” of the elements so conjoined. Other elements can optionally bepresent other than the elements specifically identified by the “and/or”clause, whether related or unrelated to those elements specificallyidentified. Thus, as a non-limiting example, a reference to “A and/orB”, when used in conjunction with open-ended language such as“comprising” can refer, in one embodiment, to A only (optionallyincluding elements other than B); in another embodiment, to B only(optionally including elements other than A); in yet another embodiment,to both A and B (optionally including other elements); etc.

As used herein in the specification and in the embodiments, “or” shouldbe understood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the embodiments, “consisting of,” will refer to the inclusion ofexactly one element of a number or list of elements. In general, theterm “or” as used herein shall only be interpreted as indicatingexclusive alternatives (i.e., “one or the other but not both”) whenpreceded by terms of exclusivity, such as “either,” “one of” “only oneof,” or “exactly one of” “Consisting essentially of,” when used in theembodiments, shall have its ordinary meaning as used in the field ofpatent law.

As used herein, the terms “about” and/or “approximately” when used inconjunction with numerical values and/or ranges generally refer to thosenumerical values and/or ranges near to a recited numerical value and/orrange. In some instances, the terms “about” and “approximately” may meanwithin ±10% of the recited value. For example, in some instances, “about100 [units]” may mean within ±10% of 100 (e.g., from 90 to 110). Theterms “about” and “approximately” may be used interchangeably.

What is claimed is:
 1. An apparatus, comprising: a virtual reality (VR)device configured to present at least one of a visual, olfactory,gustatory, auditory, or haptic signal to a user; a set of sensorsconfigured to measure user data, the set of sensors including at leastan electroencephalography (EEG) sensor and a photoplethysmography (PPG)sensor; a memory; and a processor operatively coupled to the virtualreality device, the set of sensors, and the memory, the processorconfigured to: present, using the VR device, a plurality of sets ofobjects to the user according to a sequence by: presenting, using the VRdevice, a first set of objects of the plurality of sets of objects tothe user, the first set of objects including visual, olfactory,gustatory, auditory, or haptic elements; instructing the user to engagein an activity for increasing focus or relaxation for the user; afterthe user has been instructed to engage in the activity, iterativelyperforming until a score indicative of a state of the user satisfies ametric: measuring, using the set of sensors, the user data including atleast EEG data and heart rate variability (HRV) data; determining, usinga model trained to measure the state of the user, and based on the userdata, the score of the user; and in response to the score of the userbeing above a threshold, modifying, using the VR device, thepresentation of the first set of objects such that the first set ofobjects form or follow a pattern or presenting, using the VR device, anadditional object; in response to the score of the user satisfying themetric, presenting, using the VR device, a second set of objects of theplurality of sets of objects to the user; and presenting each set ofobjects of the plurality of sets of objects subsequent to the first andsecond sets of objects in the sequence in response to the score of theuser satisfying the metric after a set of objects of the plurality ofsets of objects immediately preceding each set of objects of theplurality of set of objects in the sequence is presented.
 2. Theapparatus of claim 1, wherein the additional object is an auditoryelement including a tone, a sound effect, or music.
 3. The apparatus ofclaim 1, wherein the processor is configured to instruct the user toengage in the activity for increasing focus or relaxation for the userby: instructing the user to focus on breathing; instructing the user tofocus on a visual element or an auditory element included in the firstset of objects; instructing the user to focus on a body of the user; orperforming a combination thereof.
 4. The apparatus of claim 1, whereinthe user data further includes at least one of: heart rate data,respiratory data, PPG data, galvanic skin response data, blood glucosedata, pupillometry data, eye movement data, electromyography (EMG) data,electrodermal activity (EDA) data, blood pressure data, or bodytemperature.
 5. The apparatus of claim 1, wherein the score of the usersatisfies the metric when the score of the user remains above thethreshold for a set period of time.
 6. The apparatus of claim 1, whereinthe first set of objects includes a first set of visual elementsincluding the visual elements, the processor configured to modify thepresentation of the first set of objects by moving one or more visualelements of the first set of visual elements to form a pattern with thefirst set of visual elements.
 7. The apparatus of claim 1, wherein thefirst set of objects includes a first set of audio elements includingthe auditory elements, the processor configured to modify thepresentation of the first set of audio elements by changing an intensityor frequency of one or more audio elements of the first set of audioelements.
 8. The apparatus of claim 1, wherein the processor isconfigured to present the second set of objects while the first set ofobjects continue being presented.
 9. The apparatus of claim 1, whereinthe processor is further configured to fade away the first set ofobjects before presenting the second set of objects.
 10. The apparatusof claim 1, wherein the user data is measured at least during an initialperiod of time, the processor further configured to adjust, based on theuser data measured during the initial period of time, the threshold tochange a difficulty of satisfying the metric.
 11. The apparatus of claim10, wherein the processor is configured to adjust the threshold by:determining, based on the user data measured during the initial periodof time, a competency group for the user, the competency group beingfrom a set of competency groups; and setting the threshold based on thecompetency group determined for the user.
 12. The apparatus of claim 10,wherein the processor configured to adjust the threshold by:determining, based on the user data measured during the initial periodof time, a rate of progression through the plurality of sets of objects;and setting the threshold based on the rate of progression.
 13. Theapparatus of claim 12, wherein the processor is configured to set thethreshold based on the rate of progression by: increasing the thresholdwhen the rate of progression is greater than a predetermined rate; anddecreasing the threshold when the rate of progression is less than thepredetermined rate.
 14. The apparatus of claim 1, wherein the processoris further configured to, in response to the state of the usersatisfying the metric after the set of objects of the plurality of setsof objects last in the sequence is presented, send instructions toadminister a drug treatment to the user.
 15. The apparatus of claim 1,wherein the model is a user-specific model, the processor being furtherconfigured to, prior to presenting the plurality of sets of objects:present, during a calibration phase, a set of environmental stimulationsconsidered to be calming to the user interspersed with periods of noenvironmental stimulation; measure separate user data during thecalibration phase; and adapt, using the separate user data measuredduring the calibration phase, a generic model trained using data otherthan the separate user data to measure states of users to be theuser-specific model for measuring the state of the user.
 16. Theapparatus of claim 1, wherein the processor is further configured to,prior to presenting the plurality of sets of objects: determine whetheran impedance measured by the EEG sensor falls within a predeterminedrange of impedances; in response to the impedance being within thepredetermined range, present an indication that the EEG sensor isproperly positioned; and in response to the impedance not being withinthe predetermined range, present an indication that the EEG sensor needsto be readjusted.
 17. The apparatus of claim 1, wherein in response tothe score of the user being below the threshold, modifying, using the VRdevice, the presentation of the first set of objects such that the firstset of objects regress to an earlier state.
 18. An apparatus,comprising: a virtual reality (VR) device configured to present at leastone of a visual, olfactory, gustatory, auditory, or haptic signal to auser; one or more electroencephalography (EEG) sensors configured tomeasure EEG data of the user; a memory; and a processor operativelycoupled to the virtual reality device, the one or more EEG sensors, andthe memory, the processor configured to: present, using the VR device, aplurality of sets of objects to the user according to a sequence by:presenting, using the VR device, a first set of objects of the pluralityof sets of objects to the user, the first set of objects includingvisual, olfactory, gustatory, auditory, or haptic elements; instructingthe user to engage in an activity for increasing focus or relaxation forthe user; after the user has been instructed to engage in the activity,iteratively performing until a score indicative of a state of the usersatisfies a metric: measuring, using the one or more EEG sensors, theEEG data; determining, using a model trained to measure the state of theuser, and based on the EEG data, the score of the user; and in responseto the score of the user being above a threshold, modifying, using theVR device, the presentation of the first set of objects such that thefirst set of objects form or follow a pattern or presenting, using theVR device, an additional object; in response to the score of the usersatisfying the metric, presenting, using the VR device, a second set ofobjects of the plurality of sets of objects to the user; and presentingeach set of objects of the plurality of sets of objects subsequent tothe first and second sets of objects in the sequence in response to thescore of the user satisfying the metric after a set of objects of theplurality of sets of objects immediately preceding each set of objectsof the plurality of set of objects in the sequence is presented.
 19. Theapparatus of claim 18, wherein the additional object is an auditoryelement including a tone, a sound effect, or music.
 20. The apparatus ofclaim 18, wherein the score of the user satisfies the metric when thescore of the user remains above the threshold for a set period of time.21. The apparatus of claim 18, wherein the first set of objects includesa first set of visual elements including the visual elements, theprocessor configured to modify the presentation of the first set ofobjects by moving one or more visual elements of the first set of visualelements to form a pattern with the first set of visual elements. 22.The apparatus of claim 18, wherein the first set of objects includes afirst set of audio elements including the auditory elements, theprocessor configured to modify the presentation of the first set ofaudio elements by changing an intensity or frequency of one or moreaudio elements of the first set of audio elements.
 23. An apparatus,comprising: a virtual reality (VR) device configured to present at leastone of a visual, olfactory, gustatory, auditory, or haptic signal to auser; a set of sensors configured to measure user data, the set ofsensors including at least an electroencephalography (EEG) sensor and aphotoplethysmography (PPG) sensor; a memory; and a processor operativelycoupled to the virtual reality device, the set of sensors, and thememory, the processor configured to: present, during a calibrationphase, a set of environmental stimulations considered to be calming tothe user interspersed with periods of no environmental stimulation;measure first user data during the calibration phase; adapt, using thefirst user data measured during the calibration phase, a generic modeltrained using data other than the first user data to measure states ofusers to be a user-specific model for measuring a state of the user;present, after the calibration phase, and using the VR device, a sceneincluding a first set of objects to the user, the first set of objectsincluding visual, olfactory, gustatory, auditory, or haptic elements;instruct the user to engage in an activity for increasing focus orrelaxation for the user; after the user has been instructed to engage inthe activity, iteratively perform until a score indicative of a state ofthe user satisfies a metric: measuring, using the set of sensors, seconduser data of the user, the second user data including at least EEG dataand heart rate variability (HRV) data; determining, using theuser-specific model, and based on the second user data, the score of theuser; and in response to the score of the user being above a threshold,modifying, using the VR device, the presentation of the first set ofobjects such that the first set of objects form or follow a pattern orpresenting, using the VR device, an additional object; and in responseto the score of the user satisfying the metric, present, using the VRdevice, a second set of objects to the user.
 24. The apparatus of claim23, wherein the score of the user satisfies the metric when the score ofthe user remains above the threshold for a set period of time.
 25. Theapparatus of claim 23, wherein the first set of objects includes a firstset of visual elements including the visual elements, the processorconfigured to modify the presentation of the first set of objects bymoving one or more visual elements of the first set of visual elementsto form a pattern with the first set of visual elements.
 26. Theapparatus of claim 23, wherein the first set of objects includes a firstset of auditory elements including the audio elements, the processorconfigured to modify the presentation of the first set of audio elementsby changing an intensity or frequency of one or more audio elements ofthe first set of audio elements.
 27. An apparatus, comprising: a virtualreality (VR) device configured to present at least one of a visual,olfactory, gustatory, auditory, or haptic signal to a user; one or moreelectroencephalography (EEG) sensors configured to measure EEG data ofthe user; a memory; and a processor operatively coupled to the virtualreality device, the one or more EEG sensors, and the memory, theprocessor configured to: present, during a calibration phase, a set ofenvironmental stimulations considered to be calming to the userinterspersed with periods of no environmental stimulation; measure firstEEG data during the calibration phase; adapt, using the first EEG datameasured during the calibration phase, a generic model trained usingdata other than the first EEG data to measure states of users to be auser-specific model for measuring a state of the user; present, afterthe calibration phase, and using the VR device, a scene including afirst set of objects to the user, the first set of objects includingvisual, olfactory, gustatory, auditory, or haptic elements; instruct theuser to engage in an activity for increasing focus or relaxation for theuser; after the user has been instructed to engage in the activity,iteratively perform until a score indicative of a state of the usersatisfies a metric: measuring, using the one or more EEG sensors, secondEEG data; determining, using the user-specific model, and based on thesecond EEG data, the score of the user; and in response to the score ofthe user being above a threshold, modifying, using the VR device, thepresentation of the first set of objects such that the first set ofobjects form or follow a pattern or presenting, using the VR device, anadditional object; and in response to the score of the user satisfyingthe metric, present, using the VR device, a second set of objects to theuser.
 28. The apparatus of claim 27, wherein the score of the usersatisfies the metric when the score of the user remains above thethreshold for a set period of time.
 29. The apparatus of claim 27,wherein the first set of objects includes a first set of visual elementsincluding the visual elements, the processor configured to modify thepresentation of the first set of objects by moving one or more visualelements of the first set of visual elements to form a pattern with thefirst set of visual elements.
 30. The apparatus of claim 27, wherein thefirst set of objects includes a first set of audio elements includingthe auditory elements, the processor configured to modify thepresentation of the first set of audio elements by changing an intensityor frequency of one or more audio elements of the first set of audioelements.